Regular readers of the blog know I have written before about the potential effects of screen time on brain development. I think it’s an important issue and represents a kind of ongoing experiment in our children for which we don’t know the results. But what we do know is that excessive screen time is bad for development. The problem is we don’t know what “excessive” means in this situation. This new study brings further information to the question. The somewhat ominous title is: “Associations between screen-based media use and brain white matter integrity in preschool-aged children.”
The authors used MRI scanning, a way of imaging the brain in detail, to assess the fine details of brain structure in children who were chronically exposed to more screen time than that recommended by the American Academy of Pediatrics. (You can find the AAP recommendations here.) They studied 47 children ages 3 to 5 years. That’s not a huge number, but it’s still quite a few, considering the cost and difficulties in doing MRIs on young children. You don’t need to be a developmental neurologist to understand the implications of the findings:
In this cross-sectional study of 47 healthy prekindergarten children, screen use greater than that recommended by the American Academy of Pediatrics guidelines was associated with (1) lower measures of microstructural organization and myelination of brain white matter tracts that support language and emergent literacy skills and (2) corresponding cognitive assessments.
Although this is a small study, the take-home message to me is to take seriously the recommendations of the AAP regarding screen time for small children, certainly until we know more about what this vast, uncontrolled experiment in our children — the proliferation of screens in our daily lives — represents.
It’s widely known socioeconomic status correlates with measures of health; rich people have better overall health and even longer lifespans than do poorer people. Of course there are several reasons this might be the case, including better access to healthcare for chronic problems, better diet opportunities (Google “food deserts” to learn more about that), and better living environments. Using Medicaid as a surrogate for socioeconomic status, it’s been shown children on Medicaid are much more likely to end up in a PICU than are more affluent children. I’ve written about that before. The Medicaid data are sort of a 50,000 foot high view of the issue. Now a recent study from the Cincinnati PICU entitled “Neighborhood Poverty and Pediatric Intensive Care Use” focuses on a specific local region — a view from the ground. It provides a useful case study of the issue.
The authors looked at 4,071 admissions to the PICU that led to a total of 12,297 patient days. They only evaluated children from Hamilton County, the county around Cincinnati. They then matched the children to the poverty rates in the neighborhoods they lived in using census tracts. It’s a pretty crude, yet straightforward measure to answer the question they’re asking. The results are a bit noisy and can best be appreciated with simple scatter plots:
Child poverty was significantly associated with PICU admission (p < 0.001). When the PICU admission days were grouped into quintiles, the most affluent quintile had 23 days per 1,000 children and the poorest quintile had 82 days per 1,000 children. That’s a pretty striking difference — 350% higher in the poorest children.
I think the strength of this simple study is that it puts a local face on a phenomenon that has previously been studied by state or nationally. The implication is clear: if we want healthier children and lower healthcare costs, we should focus on childhood poverty. The returns on investing in this would be huge.
There is a troubling entity in pediatrics. Sometimes infants appear to have suffered some catastrophic problem, only to recover within minutes. Forty years ago these were termed “near miss sudden infant death syndrome events.” The notion was that they represented part of the spectrum of SIDS — sudden infant death syndrome — that had, for some reason, been averted. In the mid-1980s researchers realized these spells weren’t really related to SIDS because infants who experienced them were not at higher risk for having a real SIDS event later. A new term was coined: “Apparent life threatening event (ALTE).” The working definition was this:
An episode that is frightening to the observer and that is characterized by some combination of apnea [pause or cessation of breathing], color change (usually cyanotic [blue] or pallid but occasionally erythematous [red, flushed] or plethoric), marked change in muscle tone (usually marked limpness), choking, or gagging. In some cases the observer fears that the infant has died.
ALTE events are not only terrifying to parents; physicians are also worried and uncertain about what to do. I know — I’ve cared for many infants over the years who fit this description. It’s easy to know what to do if the infant looks abnormal when you examine him because whatever abnormality you find guides your course of action. But typically these babies look fine by the time the physician sees them. If the parents called 911, the paramedics also find a normal-appearing baby. Usually we admit such babies to the hospital and place them on heart and breathing monitors to see if they do whatever it was again. In my experience, they typically don’t. So now what? In at most half the cases we identify a probable cause, the most common of which is reflux of feedings from the stomach to the mouth, after which a small amount gets in the airway. When that happens a common infant reflex is to stop breathing — stimulating them gets them to start again. Respiratory infections (particularly RSV) and seizures, convulsions, are also potential causes. But at least half the time we have no idea what happened.
What is the risk of an ALTE recurring? It turns out we have no idea about that, either. A review of nearly 37 research articles spanning 40 years concluded such a prediction can’t be made, largely because the definitions used for what is or is not an ALTE have been quite variable in spite of the above consensus statement. A huge issue is that infants who are clearly abnormal, but who arrive for evaluation with an ALTE, are often lumped in with children who appear normal after the event. Those are very different groups.
To try to analyze this troubling condition the American Academy of Pediatrics has issued a clinical practice guideline about what to call these events and what to do about them. They did add to the alphabet soup by coining a new term: “Brief, resolved, unexplained event (BRUE).” I’m not sure how useful that term will be, but the intent is to separate out those children in which a detailed conversation with the parents and a thorough evaluation do not identify any potential cause. For those infants the committee (and it was a committee, with all the problems that can bring) recommended not doing so many tests. One thing is clear; a home apnea monitor does not at all reduce the risk of future harm, and so using one is not recommended. A BRUE (or ALTE) is not associated with risk for SIDS — that’s a key observation that has stood the test of time. We do know one thing that is clearly associated with SIDS: putting babies to sleep on their stomachs. Ever since the introduction of the “back to sleep program,” of instructing parents to put their babies to sleep lying on their backs, the incidence of SIDS has dropped remarkably, although it still happens.
What do I think? We don’t know what causes these spells. However, premature infants are at higher risk for them and we know such infants often have disordered breathing and heart reflexes. I have no data at all about it, but my guesswork opinion is these spells represent immaturity of infants’ brains such that they respond to a variety of stimuli by pausing or stopping breathing or slowing their heart rate. The tendency goes away with growing older because we only see this problem in infants. I hope this new categorization leads to useful research about what is really happening during these mysterious events. Of course simply renaming ALTE a BRUE doesn’t really help our understanding of all this.
The debate over the safety of giving birth at home, both for the mother and for the infant, has continued for years. I’ve written about the issue myself. From time immemorial until about 75 years ago or so most babies were born at home. Now it’s around 1% in the USA, although it’s much higher than that in many Western European countries. The shift to hospital births paralleled the growth of hospitals, pediatrics, and obstetrics. With that shift there has been a perceived decrease in women’s autonomy over their healthcare decisions. There has also been an unsurprising jump in the proportion of caesarian section deliveries, an operative procedure, and various other medical interventions in labor and delivery, even though current data suggests the recent jump in caesarian delivery (now around 30%) has not added any benefits. The debate over whether the dominance of hospital births is a good thing or a bad thing (or neither) is much more than a medical debate; it’s also a social and political one. It’s also to some extent an issue of medical power, a struggle between physician obstetricians who deliver babies in the hospital and nurse midwives who often deliver babies at home. I’m very interested in the social and political aspects, but as a pediatrician I’m particularly concerned with the safety question: Is it more dangerous for your baby to be born at home?
One problem in answering this question is that most of the studies about the safety of home birth came from abroad. But a few years ago we got some data from the USA, published in the New England Journal of Medicine, entitled “Planned out of hospital births and birth outcomes.”
One big problem with evaluating previous data has been that vital statistics from birth certificates counted home births and hospital births, but did not identify as a separate category those women who planned to deliver at home, but then were admitted to a hospital to deliver there because of some issue with the pregnancy. Such women were just counted as hospital births. Also, the recent growth of birthing centers has introduced a location kind of intermediate between home and hospital. The large study linked above was from Oregon, using the years 2012 and 2013. It gives some useful information.
The bottom line is that children born to women who intended to give birth at home had an infant mortality rate of 3.9 deaths per 1,000 deliveries. This was significantly higher than the death rate of infants born in a hospital, which was 1.8 deaths per 1,000 deliveries. Not surprisingly, women who delivered in the hospital had a far high rate of some kind of intervention, such as caesarian section.
What should we make of this? Thinking about risk can be difficult, and it’s important to understand the difference between relative and absolute risk. (I’ve written about that, too.) Media reports often obscure this key point. For example, in this study the risk of infant mortality increased 100% with home birth. 100%!! But twice a very small number is still a very small number. The absolute risk of a baby dying in a home delivery is very small. Still, it is higher.
What this means is that a woman deciding to deliver at home should understand all the facts. Some will not want to accept this increased risk, however small it is in absolute terms. Some will accept it. The same issue of the Journal had a good editorial discussing how to think about the issue. It’s a very good summary of the fundamental question. It’s all about the issue of acceptable risk, and how that varies with the person. The conclusion:
Ultimately, women’s choices for place of delivery will be determined by the extent of their tolerance for risk and which risks they most want to avoid.
All of us are aware of what has been termed our “obesity epidemic.” The current prevalence of obesity among adults in the US is around 40%, a dramatic increase over the past 50 years; it was about 15% in 1970. Rates are also increasing across first world countries, so we are not alone in this. Obesity is defined as a body mass index (BMI) of greater than 30. Values of 25 – 30 are termed overweight. BMI is weight in kilograms divided by height in meters squared.
The graph shows the trends over the past decades and has some interesting features. Note that the percent of the population that is obese or extremely obese (BMI > 40) has increased but the percent classified as overweight has not. This suggests to me, although I haven’t seen anything written about it, that overweight and obese patients are two separate groups; the overweight are not destined to become obese. There are even some recent data that suggest being mildly overweight may actually be a good thing as you age.
Many explanations have been offered for the progressive increase in adult obesity, including increased intake of calories, often in the form of soft drinks, and sedentary lifestyle. The simple calculation of excess calories consumed versus calories burned offers a partial explanation, and certainly that’s what I was taught in medical school in the 1970s; obesity was simple arithmetic. It turns out things are more complicated than that. Genetics, for example, plays a large role, as do various hormonal systems.
I don’t follow the enormous medical literature on obesity closely, but this recent study really intrigued me. It was in a journal I haven’t seen before, Economics and Human Biology. This seems appropriate, since the economic effects of the obesity epidemic are massive and getting larger all the time. The authors studied annual sugar consumption in the US population and compared it with obesity rates later. Now, that approach is pretty reductionist in that it ignores many other kinds of calories that aren’t sugar, but the results are interesting. Their findings suggest that, among today’s adults, obesity correlates with global sugar intake during their childhood years in the 1970s and 1980s. If this is the case, one would predict a decrease in obesity among adolescents and young adults now because sugar intake in the US has decreased by 25% in the last decade. In fact, adolescent obesity prevalence, after a steady and seemingly inexorable rise, may actually have plateaued over the past 5 years or so.
The usual caveat of correlation not indicating causation need to be kept in mind, of course. Yet it makes biological sense to me. I think our metabolic state could have a certain kind of “memory” about the milieu it experienced during early growth and development and have responded to that it ways that could persist for many years.
I have written previously on Kevin Pho’s useful KevinMD site about the alarming statistic that gunshot injuries are now the second most common cause of death among children. Between 2011 and 2015 there were over 21,000 children killed by guns. This recent study in the Pediatrics, the journal of the American Academy of Pediatrics, further analyzes the question; it compares pediatric firearm fatality rates among the various states and then tests for correlation between children killed and the degree of strictness or not of the state’s gun laws. There are extensive data on whole populations showing that stricter laws correlate with lower rates of firearms injuries, but pediatric fatalities have not been specifically investigated. Of course as a PICU physician, somebody who takes care of children shot by guns, the latter question is of great interest to me.
Central to the work is developing some sort of grading scale for strictness of gun laws. The authors didn’t use a scale they developed themselves — they used the 2011–2015 Gun Law Scorecard system from the Brady Campaign to Prevent Gun Violence. The higher the state gun law score, the stricter the firearms legislation. The authors also used what they termed secondary exposure variables. These were the presence or not of individual laws previously associated with lower mortality rates in the total population of adults and children: universal background checks for firearm purchase, universal background checks for ammunition purchase, and identification requirement for firearms (microstamping, ballistic fingerprinting). Their findings are best summarized in this graph from the paper. It plots gun law score against the rate of children killed by guns.
The trend line for that graph is pretty striking. It’s clear increasing strictness of gun laws correlates with less children killed by guns. This also fits with the experience in other Western countries. See the example of Australia tightening their gun laws, which greatly reduced gun deaths.
The issue, of course, is that America has a gun culture unique in the Western world. Our courts have interpreted the rights of citizens to own guns quite broadly owing to the Second Amendment. It’s known the great majority of guns are owned by a minority of citizens. So the question is: How many dead children represent an acceptable price to pay for loose gun laws? Because it’s clear that, all slogans aside (e.g. “guns don’t kill people, people kill people”), looser laws lead to more deaths. The research is there. Now we have to decide if we want to do anything about it.
Every day we get bombarded in the news with health statistics. Coffee causes cancer! Coffee cures cancer! And so on. Many of these are meant to grab headlines (and, these days, web page clicks) and the articles they accompany are often very poor at telling the reader what they mean. They often have statistics, and health statistics can be complicated. Sad to say, even many physicians are pretty poor and sorting out the hype from the helpful. This article is a very helpful guide to finding your way when you are reading the health news. It’s called “Helping Doctors and Patients Make Sense of Health Statistics,” and it does just that. I’d bookmark it or even print it out for future reference, if you’re more old school. Don’t be put off by the somber looking first page — it’s actually quite readable. I should point out here that, although I took statistics courses long ago and have used simple statistical tests in my own research career, I am by no means expert. I always consulted a real statistician before submitting any research for publication.
The article starts with the common problem one sees in media reports: the difference between absolute and relative risk. The authors used the example of a scare over birth control pills that happened in 1995 when the U.K. Committee on Safety in Medicines issued a warning about a newer version of pills. The committee sent a warning to all physicians that the newer pills were associated with a 100% rise in risk for serious blood clots. One hundred percent! Yikes! The warning led to many women stopping their pills and there was a predictable rise in unwanted pregnancies, accompanied by an estimated 15,000 more abortions the following year. The effects lasted for years. What was the truth about this new risk?
The truth was that the newer pills were associated with a risk for serious blood clots of 2 per 7,000 women. For comparison, the earlier pills had been associated with a 1 per 7,000 women risk. And 2 is 100% more than 1, so that’s the increase in relative risk. But the increase in absolute risk was an additional 1 woman in 7,000. (It should be noted here that pregnancy itself is associated with an increased risk of blood clots.) I see this particular misunderstanding often in news reports regarding risks of medical procedures. When you read these stories you need to examine not just the relative risk, which often makes for good headlines; you need to look at the actual number, not just the percent change.
The pill scare hurt women, hurt the National Health Service, and even hurt the pharmaceutical industry. Among the few to profit were the journalists who got the story on the front page.
There is also this helpful article, from the always readable Scientific American. I like it a lot, too. It reminds us how statistical significance doesn’t always mean real life significance.
Imagine if there were a simple single statistical measure everybody could use with any set of data and it would reliably separate true from false. Oh, the things we would know! Unrealistic to expect such wizardry though, huh? Yet, statistical significance is commonly treated as though it is that magic wand. Take a null hypothesis or look for any association between factors in a data set and abracadabra! Get a “p value” over or under 0.05 and you can be 95% certain it’s either a fluke or it isn’t. You can eliminate the play of chance! You can separate the signal from the noise! Except that you can’t. That’s not really what testing for statistical significance does. And therein lies the rub.
The article points out what the vaunted, and too often venerated, p value means is that it estimates the probability of getting roughly that result if the study hypothesis is assumed to be true. It can’t on its own tell you whether this assumption was right, or whether the results would hold true in different circumstances. It provides a limited picture of probability, taking limited information about the data into account and giving only “yes” or “no” as options.
If you are really interested in this topic you should read a bit about what’s called Bayesian statistics, named after an 18th century mathematician. The basic notion here is that we need to consider our prior knowledge about something before applying statistical tests, and that we should factor in this knowledge when we make our statistical comparisons. In other words, all possibilities are not intrinsically equal going into the analysis. The debates between Bayesian and what are termed “frequentist” statisticians go back and forth. But what we should take home from these debates is that the science of statistics, like other sciences, is subject to revision and change over time.
A final key point is to look at medical headlines of new medical breakthroughs and try to decide if the findings really are “significant” in real life. Is there only a tiny effect, really, even though the p value is “significant” at the 0.05 level? Also beware of what we call “data dredging,” in which multiple comparisons are made using the same data set. When you do that the chances of coming up with a significant, yet spurious association go up.
All of this has made some people call for some rudimentary statistical training to be part of the standard mathematics curriculum at the high school level. I think this is a good idea. I didn’t get introduced to any statistical concepts in high school, and I took all the math available. That should change if we expect the mass of our citizenry to be competent to judge things for themselves. Medical journalists definitely need this knowledge because currently many do a terrible job interpreting medical reports. The two articles I linked are a great place to start.
We believe that the current entrepreneurial development model for antibiotics is broken and needs to be fundamentally transformed.
This provocative opinion is from a recent editorial in The New England Journal of Medicine. The introduction of penicillin, the first antibiotic miracle drug, led to an 80% reduction in mortality from infectious diseases. Other antibiotics quickly followed, reducing death rates even further. Over the past several decades, however, the discovery of new antibiotics has greatly slowed; most are what are called “me too” drugs that are potentially profitable for the manufacturer but not in any way ground-breaking. Emerging resistance to antibiotics was noted soon after their discovery but newer agents appeared to keep us one step ahead of the pathogens. This breathing room may now have disappeared — we now are confronted with pathogenic bacteria that are completely resistant to all known antibiotics. Some have termed our new situation the post-antibiotic era. So we desperately need newer agents to treat infection. Where are these to come from?
The editorial writers describe how ineffectual our current model is for developing these essential new antibiotics. For one thing, development costs are enormous — up to two billion dollars. There is also this problem:
Rising rates of resistance appear to create new market opportunities for antibiotics. However, the absolute number of infections caused by each type of resistant bacterium is relatively small. Each newly approved antibiotic thus captures an ever-shrinking share of an increasingly splintered market — a problem that will only worsen over time.
A widely influential solution was proposed by the economist Jim O’Neill in 2016. His idea was to offer a variety of special financial incentives to drug companies to develop new antibiotics. Now he says it’s simply time to “just take it away from them and take it over.” The authors of The New England Journal editorial propose a model consisting of nonprofit organizations to focus on all aspects of preventing infections — not just new antibiotics but also things like vaccines, immunotherapies, and inflammatory modulators. I agree such a multifaceted approach is important because resistance among bacteria will always be an issue. A key principle here is using multiple approaches that work in different ways. We certainly use that principle in infectious disease practice by combining antibiotics that work in different ways.
Establishing new ways of organizing our fight against infection would be difficult. But market-based, for-profit approaches simply haven’t worked at all. Drug companies are actually losing money trying. And thus as a society we’re losing the battle.
This one isn’t really about children specifically, but I found it fascinating. It recently appeared in the prestigious scientific journal Nature. Humans like music. The kind of music we like varies greatly, but love of music and rhythm seems to be something that crosses all cultural boundaries. Why is this? It would appear to be something intrinsic to being human, which implies that love of music is hard wired into our brains. So why is that? Until I read the above article in Lancet I was unaware there is an entire scientific field regarding the neuroscience of music appreciation. Some experts in this field believe music appeared even before the maturation of language:
Somewhere along the evolutionary way, our ancestors, with very limited language but with considerable emotional expression, began to articulate and gesticulate feelings: denotation before connotation. But, as the philosopher Susanne Langer noted, ‘The most highly developed type of such purely connotational semantic is music.’ In other words, meaning in music came to us before meaning given by words.
The authors of the Lancet study investigated the response to various musical things in macaque monkeys and compared them to those in human brains. There are portions of the brain that are devoted to perception of musical pitch and the investigators used those areas for comparison. The research team set out to compare how the brains of humans and those of rhesus macaques reacted to auditory stimuli that characterize music and speech. Speech and music contain harmonic frequency components, which are perceived to have pitch. Highly inflected human languages in particular, such as Chinese, rely on pitch and tone to convey meaning, and humans recognize this very early in life. Humans have cortical regions with a strong response preference for harmonic tones versus noise. But is the same true for nonhuman primates? The answer was no. In their words: “The results raise the possibility that these sounds, which are embedded in speech and music, may have shaped the basic organization of the human brain.”
Humans but not macaques showed regions with a strong preference for harmonic sounds compared to noise, measured with both synthetic tones and macaque vocalizations. . . . This species difference may be driven by the unique demands of speech and music perception in humans.
So OK — we differ from monkeys. But pet owners will tell you that other animals besides us are affected by music, although a dog howling along like the one above. Some research suggests dogs find classical music calming and heavy metal rock music annoying (just like me!). As a pediatrician, and like many parents, I have noticed infants respond to music. This begins so early that it strongly suggests to me the circuits to respond to pitch and harmony are already hard-wired into the human brain at birth.
At any rate, this little excursion is a great example of why it’s fascinating to peruse from time to time general scientific journals. You come across things you never otherwise would have encountered.
I wrote about this topic a few years back, but the recent outbreak of measles has once again ignited the debate of just what the government has the right to do or not do in compelling individual actions in support of public health. This is an old question, and it’s worth considering it in historical context.
One aspect of the endless vaccine debate is the aspect of coercion some parents feel about requiring children to be vaccinated before they can go to school. Vaccination is mandated for school attendance in all states. But this isn’t really an absolute requirement. Although all 50 states ostensibly require vaccination, all but 5 (Mississippi, West Virginia, and more recently California, New York, and Maine) allow parents to opt out for religious reasons, and 15 states allow this for philosophical reasons. (See here for a current list.) Still, in general vaccines are required unless the child has a medical reason not to get them, such as having a problem with the immune system. Is this an unprecedented use of state power? I don’t think it is.
In fact, historically there have been many examples of the government inserting itself into healthcare decisions of individuals and families in order to protect the public health. Some of these go back many years. Quarantine, for example, goes back to medieval times, centuries before the germs were discovered. It has since 1944 been a power of the federal government; federal agents may detain and send for medical examination persons entering the country suspected of carrying one of a list of communicable diseases. Quarantine has also been used by local and state governments, particularly in the pre-antibiotic era. Measles is a good example, as you can see from the photograph above. Quarantine can be abused, and has in fact been abused in the past for discrimination against certain minority groups. A paper from the American Bar Association details some of those instances here. The paper even questions if it should be abolished for these reasons. But the practice is a very old one.
Seaports have long been sites of quarantine enforcement. In colonial Pennsylvania, for example, ships bound for Philadelphia had to stop at an island in the Delaware River for up to 30 days to ensure they were not carrying any disease. Note that an island was chosen since it is easier to isolate ships there. During the cholera epidemics in the mid-nineteenth century the quarantine of ships arriving from abroad was common. It should be noted though that, prior to the acceptance of the germ theory, enforcement of quarantine was at least as concerned with the cargo as it was the persons on the ship. But the legality of the practice was well accepted.
Laws requiring mandatory vaccination have been around for over a century. Some opposition to them has been around just as long. The constitutionality of these laws was affirmed in the famous decision by the Supreme Court in 1905 — Jacobson v. Massachusetts. The case in question concerned smallpox vaccination, and of course we have many more vaccinations now. There have since been multiple cases concerning mandatory vaccination for school attendance; in all cases the courts have ruled in favor of these mandates. If you are interested in reading deeper into this controversy a good person to consult is Professor Dorit Reiss, a law professor at the University of California. She has devoted her career to examining vaccine law and politics. Here is a place where you can find links to some of her many publications on the subject. You can also read a nice review of the historical controversies over quarantine here.
Of course the government mandates many things for the protection of public health. Milk is pasteurized (although there are raw milk enthusiasts who object — and many of whom get sick as a result), water is purified, and dirty restaurants can be closed. Like quarantine, these measures restrict our personal freedom a little, but what about government-mandated medical treatment? That sounds a bit more like the situation with compulsory vaccination of children. As it happens, there are more recent examples of compulsory treatment, particularly involving tuberculosis.
A couple of decades ago I was involved in a case of a woman with active tuberculosis who refused to take treatment for it. Worse, her particular strain of TB was one highly resistant to many antibiotics, so if that spread it would represent a real public health emergency. The district judge agreed. He confined the woman to the hospital against her will so she could be given anti-TB medications until she was not longer infectious to others. At the time I thought this was pretty unusual. When I looked into it, though, I found that there have been many instances of people with TB being confined against their will until they were no longer a threat to others. The ABA link above lists several examples of this.
So it’s clear to me there is a long tradition of the state restricting personal freedom in the service of protecting the public health. Like everything, of course, the devil is in the details. To me the guiding principle is that your right to swing your fist ends where my nose begins.
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