Researchers Behaving Badly: Known Frauds Are "the Tip of the Iceberg"
Last week, the whistleblowers in the Paolo Macchiarini affair at Sweden's Karolinska Institutet went on the record here to detail the retaliation they suffered for trying to expose a star surgeon's appalling research misconduct.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise.
The whistleblowers had discovered that in six published papers, Macchiarini falsified data, lied about the condition of patients and circumvented ethical approvals. As a result, multiple patients suffered and died. But Karolinska turned a blind eye for years.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise. Just this week, for example, Retraction Watch and STAT together broke the news that a Harvard Medical School cardiologist and stem cell researcher, Piero Anversa, falsified data in a whopping 31 papers, which now have to be retracted. Anversa had claimed that he could regenerate heart muscle by injecting bone marrow cells into damaged hearts, a result that no one has been able to duplicate.
A 2009 study published in the Public Library of Science (PLOS) found that about two percent of scientists admitted to committing fabrication, falsification or plagiarism in their work. That's a small number, but up to one third of scientists admit to committing "questionable research practices" that fall into a gray area between rigorous accuracy and outright fraud.
These dubious practices may include misrepresentations, research bias, and inaccurate interpretations of data. One common questionable research practice entails formulating a hypothesis after the research is done in order to claim a successful premise. Another highly questionable practice that can shape research is ghost-authoring by representatives of the pharmaceutical industry and other for-profit fields. Still another is gifting co-authorship to unqualified but powerful individuals who can advance one's career. Such practices can unfairly bolster a scientist's reputation and increase the likelihood of getting the work published.
The above percentages represent what scientists admit to doing themselves; when they evaluate the practices of their colleagues, the numbers jump dramatically. In a 2012 study published in the Journal of Research in Medical Sciences, researchers estimated that 14 percent of other scientists commit serious misconduct, while up to 72 percent engage in questionable practices. While these are only estimates, the problem is clearly not one of just a few bad apples.
In the PLOS study, Daniele Fanelli says that increasing evidence suggests the known frauds are "just the 'tip of the iceberg,' and that many cases are never discovered" because fraud is extremely hard to detect.
Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
In addition, it's likely that most cases of scientific misconduct go unreported because of the high price of whistleblowing. Those in the Macchiarini case showed extraordinary persistence in their multi-year campaign to stop his deadly trachea implants, while suffering serious damage to their careers. Such heroic efforts to unmask fraud are probably rare.
To make matters worse, there are numerous players in the scientific world who may be complicit in either committing misconduct or covering it up. These include not only primary researchers but co-authors, institutional executives, journal editors, and industry leaders. Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
Another part of the problem is that it's rare for students in science and medicine to receive an education in ethics. And studies have shown that older, more experienced and possibly jaded researchers are more likely to fudge results than their younger, more idealistic colleagues.
So, given the steep price that individuals and institutions pay for scientific misconduct, what compels them to go down that road in the first place? According to the JRMS study, individuals face intense pressures to publish and to attract grant money in order to secure teaching positions at universities. Once they have acquired positions, the pressure is on to keep the grants and publishing credits coming in order to obtain tenure, be appointed to positions on boards, and recruit flocks of graduate students to assist in research. And not to be underestimated is the human ego.
Paolo Macchiarini is an especially vivid example of a scientist seeking not only fortune, but fame. He liberally (and falsely) claimed powerful politicians and celebrities, even the Pope, as patients or admirers. He may be an extreme example, but we live in an age of celebrity scientists who bring huge amounts of grant money and high prestige to the institutions that employ them.
The media plays a significant role in both glorifying stars and unmasking frauds. In the Macchiarini scandal, the media first lifted him up, as in NBC's laudatory documentary, "A Leap of Faith," which painted him as a kind of miracle-worker, and then brought him down, as in the January 2016 documentary, "The Experiments," which chronicled the agonizing death of one of his patients.
Institutions can also play a crucial role in scientific fraud by putting more emphasis on the number and frequency of papers published than on their quality. The whole course of a scientist's career is profoundly affected by something called the h-index. This is a number based on both the frequency of papers published and how many times the papers are cited by other researchers. Raising one's ranking on the h-index becomes an overriding goal, sometimes eclipsing the kind of patient, time-consuming research that leads to true breakthroughs based on reliable results.
Universities also create a high-pressured environment that encourages scientists to cut corners. They, too, place a heavy emphasis on attracting large monetary grants and accruing fame and prestige. This can lead them, just as it led Karolinska, to protect a star scientist's sloppy or questionable research. According to Dr. Andrew Rosenberg, who is director of the Center for Science and Democracy at the U.S.-based Union of Concerned Scientists, "Karolinska defended its investment in an individual as opposed to the long-term health of the institution. People were dying, and they should have outsourced the investigation from the very beginning."
Having institutions investigate their own practices is a conflict of interest from the get-go, says Rosenberg.
Scientists, universities, and research institutions are also not immune to fads. "Hot" subjects attract grant money and confer prestige, incentivizing scientists to shift their research priorities in a direction that garners more grants. This can mean neglecting the scientist's true area of expertise and interests in favor of a subject that's more likely to attract grant money. In Macchiarini's case, he was allegedly at the forefront of the currently sexy field of regenerative medicine -- a field in which Karolinska was making a huge investment.
The relative scarcity of resources intensifies the already significant pressure on scientists. They may want to publish results rapidly, since they face many competitors for limited grant money, academic positions, students, and influence. The scarcity means that a great many researchers will fail while only a few succeed. Once again, the temptation may be to rush research and to show it in the most positive light possible, even if it means fudging or exaggerating results.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable.
Intense competition can have a perverse effect on researchers, according to a 2007 study in the journal Science of Engineering and Ethics. Not only does it place undue pressure on scientists to succeed, it frequently leads to the withholding of information from colleagues, which undermines a system in which new discoveries build on the previous work of others. Researchers may feel compelled to withhold their results because of the pressure to be the first to publish. The study's authors propose that more investment in basic research from governments could alleviate some of these competitive pressures.
Scientific journals, although they play a part in publishing flawed science, can't be expected to investigate cases of suspected fraud, says the German science blogger Leonid Schneider. Schneider's writings helped to expose the Macchiarini affair.
"They just basically wait for someone to retract problematic papers," he says.
He also notes that, while American scientists can go to the Office of Research Integrity to report misconduct, whistleblowers in Europe have no external authority to whom they can appeal to investigate cases of fraud.
"They have to go to their employer, who has a vested interest in covering up cases of misconduct," he says.
Science is increasingly international. Major studies can include collaborators from several different countries, and he suggests there should be an international body accessible to all researchers that will investigate suspected fraud.
Ultimately, says Rosenberg, the scientific system must incorporate trust. "You trust co-authors when you write a paper, and peer reviewers at journals trust that scientists at research institutions like Karolinska are acting with integrity."
Without trust, the whole system falls apart. It's the trust of the public, an elusive asset once it has been betrayed, that science depends upon for its very existence. Scientific research is overwhelmingly financed by tax dollars, and the need for the goodwill of the public is more than an abstraction.
The Macchiarini affair raises a profound question of trust and responsibility: Should multiple co-authors be held responsible for a lead author's misconduct?
Karolinska apparently believes so. When the institution at last owned up to the scandal, it vindictively found Karl Henrik-Grinnemo, one of the whistleblowers, guilty of scientific misconduct as well. It also designated two other whistleblowers as "blameworthy" for their roles as co-authors of the papers on which Macchiarini was the lead author.
As a result, the whistleblowers' reputations and employment prospects have become collateral damage. Accusations of research misconduct can be a career killer. Research grants dry up, employment opportunities evaporate, publishing becomes next to impossible, and collaborators vanish into thin air.
Grinnemo contends that co-authors should only be responsible for their discrete contributions, not for the data supplied by others.
"Different aspects of a paper are highly specialized," he says, "and that's why you have multiple authors. You cannot go through every single bit of data because you don't understand all the parts of the article."
This is especially true in multidisciplinary, translational research, where there are sometimes 20 or more authors. "You have to trust co-authors, and if you find something wrong you have to notify all co-authors. But you couldn't go through everything or it would take years to publish an article," says Grinnemo.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable. Along with increased support from governments and industry, a change in academic culture that emphasizes quality over quantity of published studies could help encourage meritorious research.
But beyond that, trust will always play a role when numerous specialists unite to achieve a common goal: the accumulation of knowledge that will promote human health, wealth, and well-being.
[Correction: An earlier version of this story mistakenly credited The New York Times with breaking the news of the Anversa retractions, rather than Retraction Watch and STAT, which jointly published the exclusive on October 14th. The piece in the Times ran on October 15th. We regret the error.]
The Death Predictor: A Helpful New Tool or an Ethical Morass?
Whenever Eric Karl Oermann has to tell a patient about a terrible prognosis, their first question is always: "how long do I have?" Oermann would like to offer a precise answer, to provide some certainty and help guide treatment. But although he's one of the country's foremost experts in medical artificial intelligence, Oermann is still dependent on a computer algorithm that's often wrong.
Doctors are notoriously terrible at guessing how long their patients will live.
Artificial intelligence, now often called deep learning or neural networks, has radically transformed language and image processing. It's allowed computers to play chess better than the world's grand masters and outwit the best Jeopardy players. But it still can't precisely tell a doctor how long a patient has left – or how to help that person live longer.
Someday, researchers predict, computers will be able to watch a video of a patient to determine their health status. Doctors will no longer have to spend hours inputting data into medical records. And computers will do a better job than specialists at identifying tiny tumors, impending crises, and, yes, figuring out how long the patient has to live. Oermann, a neurosurgeon at Mount Sinai, says all that technology will allow doctors to spend more time doing what they do best: talking with their patients. "I want to see more deep learning and computers in a clinical setting," he says, "so there can be more human interaction." But those days are still at least three to five years off, Oermann and other researchers say.
Doctors are notoriously terrible at guessing how long their patients will live, says Nigam Shah, an associate professor at Stanford University and assistant director of the school's Center for Biomedical Informatics Research. Doctors don't want to believe that their patient – whom they've come to like – will die. "Doctors over-estimate survival many-fold," Shah says. "How do you go into work, in say, oncology, and not be delusionally optimistic? You have to be."
But patients near the end of life will get better treatment – and even live longer – if they are overseen by hospice or palliative care, research shows. So, instead of relying on human bias to select those whose lives are nearing their end, Shah and his colleagues showed that they could use a deep learning algorithm based on medical records to flag incoming patients with a life expectancy of three months to a year. They use that data to indicate who might need palliative care. Then, the palliative care team can reach out to treating physicians proactively, instead of relying on their referrals or taking the time to read extensive medical charts.
But, although the system works well, Shah isn't yet sure if such indicators actually get the appropriate patients into palliative care. He's recently partnered with a palliative care doctor to run a gold-standard clinical trial to test whether patients who are flagged by this algorithm are indeed a better match for palliative care.
"What is effective from a health system perspective might not be effective from a treating physician's perspective and might not be effective from the patient's perspective," Shah notes. "I don't have a good way to guess everybody's reaction without actually studying it." Whether palliative care is appropriate, for instance, depends on more than just the patient's health status. "If the patient's not ready, the family's not ready and the doctor's not ready, then you're just banging your head against the wall," Shah says. "Given limited capacity, it's a waste of resources" to put that person in palliative care.
The algorithm isn't perfect, but "on balance, it leads to better decisions more often."
Alexander Smith and Sei Lee, both palliative care doctors, work together at the University of California, San Francisco, to develop predictions for patients who come to the hospital with a complicated prognosis or a history of decline. Their algorithm, they say, helps decide if this patient's problems – which might include diabetes, heart disease, a slow-growing cancer, and memory issues – make them eligible for hospice. The algorithm isn't perfect, they both agree, but "on balance, it leads to better decisions more often," Smith says.
Bethany Percha, an assistant professor at Mount Sinai, says that an algorithm may tell doctors that their patient is trending downward, but it doesn't do anything to change that trajectory. "Even if you can predict something, what can you do about it?" Algorithms may be able to offer treatment suggestions – but not what specific actions will alter a patient's future, says Percha, also the chief technology officer of Precise Health Enterprise, a product development group within Mount Sinai. And the algorithms remain challenging to develop. Electronic medical records may be great at her hospital, but if the patient dies at a different one, her system won't know. If she wants to be certain a patient has died, she has to merge social security records of death with her system's medical records – a time-consuming and cumbersome process.
An algorithm that learns from biased data will be biased, Shah says. Patients who are poor or African American historically have had worse health outcomes. If researchers train an algorithm on data that includes those biases, they get baked into the algorithms, which can then lead to a self-fulfilling prophesy. Smith and Lee say they've taken race out of their algorithms to avoid this bias.
Age is even trickier. There's no question that someone's risk of illness and death goes up with age. But an 85-year-old who breaks a hip running a marathon should probably be treated very differently than an 85-year-old who breaks a hip trying to get out of a chair in a dementia care unit. That's why the doctor can never be taken out of the equation, Shah says. Human judgment will always be required in medical care and an algorithm should never be followed blindly, he says.
Experts say that the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully.
Researchers are also concerned that their algorithms will be used to ration care, or that insurance companies will use their data to justify a rate increase. If an algorithm predicts a patient is going to end up back in the hospital soon, "who's benefitting from knowing a patient is going to be readmitted? Probably the insurance company," Percha says.
Still, Percha and others say, the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully. "These are new and exciting tools that have a lot of potential uses. We need to be conscious about how to use them going forward, but it doesn't mean we shouldn't go down this road," she says. "I think the potential benefits outweigh the risks, especially because we've barely scratched the surface of what big data can do right now."
“Young Blood” Transfusions Are Not Ready For Primetime – Yet
The world of dementia research erupted into cheers when news of the first real victory in a clinical trial against Alzheimer's Disease in over a decade was revealed last October.
By connecting the circulatory systems of a young and an old mouse, the regenerative potential of the young mouse decreased, and the old mouse became healthier.
Alzheimer's treatments have been famously difficult to develop; 99 percent of the 200-plus such clinical trials since 2000 have utterly failed. Even the few slight successes have failed to produce what is called 'disease modifying' agents that really help people with the disease. This makes the success, by the midsize Spanish pharma company Grifols, worthy of special attention.
However, the specifics of the Grifols treatment, a process called plasmapheresis, are atypical for another reason - they did not give patients a small molecule or an elaborate gene therapy, but rather simply the most common component of normal human blood plasma, a protein called albumin. A large portion of the patients' normal plasma was removed, and then a sterile solution of albumin was infused back into them to keep their overall blood volume relatively constant.
So why does replacing Alzheimer's patients' plasma with albumin seem to help their brains? One theory is that the action is direct. Alzheimer's patients have low levels of serum albumin, which is needed to clear out the plaques of amyloid that slowly build up in the brain. Supplementing those patients with extra albumin boosts their ability to clear the plaques and improves brain health. However, there is also evidence suggesting that the problem may be something present in the plasma of the sick person and pulling their plasma out and replacing it with a filler, like an albumin solution, may be what creates the purported benefit.
This scientific question is the tip of an iceberg that goes far beyond Alzheimer's Disease and albumin, to a debate that has been waged on the pages of scientific journals about the secrets of using young, healthy blood to extend youth and health.
This debate started long before the Grifols data was released, in 2014 when a group of researchers at Stanford found that by connecting the circulatory systems of a young and an old mouse, the regenerative potential of the young mouse decreased, and the old mouse became healthier. There was something either present in young blood that allowed tissues to regenerate, or something present in old blood that prevented regeneration. Whatever the biological reason, the effects in the experiment were extraordinary, providing a startling boost in health in the older mouse.
After the initial findings, multiple research groups got to work trying to identify the "active factor" of regeneration (or the inhibitor of that regeneration). They soon uncovered a variety of compounds such as insulin-like growth factor 1 (IGF1), CCL11, and GDF11, but none seemed to provide all the answers researchers were hoping for, with a number of high-profile retractions based on unsound experimental practices, or inconclusive data.
Years of research later, the simplest conclusion is that the story of plasma regeneration is not simple - there isn't a switch in our blood we can flip to turn back our biological clocks. That said, these hypotheses are far from dead, and many researchers continue to explore the possibility of using the rejuvenating ability of youthful plasma to treat a variety of diseases of aging.
But the bold claims of improved vigor thanks to young blood are so far unsupported by clinical evidence.
The data remain intriguing because of the astounding results from the conjoined circulatory system experiments. The current surge in interest in studying the biology of aging is likely to produce a new crop of interesting results in the next few years. Both CCL11 and GDF11 are being researched as potential drug targets by two startups, Alkahest and Elevian, respectively.
Without clarity on a single active factor driving rejuvenation, it's tempting to try a simpler approach: taking actual blood plasma provided by young people and infusing it into elderly subjects. This is what at least one startup company, Ambrosia, is now offering in five commercial clinics across the U.S. -- for $8,000 a liter.
By using whole plasma, the idea is to sidestep our ignorance, reaping the benefits of young plasma transfusion without knowing exactly what the active factors are that make the treatment work in mice. This space has attracted both established players in the plasmapheresis field – Alkahest and Grifols have teamed up to test fractions of whole plasma in Alzheimer's and Parkinson's – but also direct-to-consumer operations like Ambrosia that just want to offer patients access to treatments without regulatory oversight.
But the bold claims of improved vigor thanks to young blood are so far unsupported by clinical evidence. We simply haven't performed trials to test whether dosing a mostly healthy person with plasma can slow down aging, at least not yet. There is some evidence that plasma replacement works in mice, yes, but those experiments are all done in very different systems than what a human receiving young plasma might experience. To date, I have not seen any plasma transfusion clinic doing young blood plasmapheresis propose a clinical trial that is anything more than a shallow advertisement for their procedures.
The efforts I have seen to perform prophylactic plasmapheresis will fail to impact societal health. Without clearly defined endpoints and proper clinical trials, we won't know whether the procedure really lowers the risk of disease or helps with conditions of aging. So even if their hypothesis is correct, the lack of strong evidence to fall back on means that the procedure will never spread beyond the fringe groups willing to take the risk. If their hypothesis is wrong, then people are paying a huge amount of money for false hope, just as they do, sadly, at the phony stem cell clinics that started popping up all through the 2000s when stem cell hype was at its peak.
Until then, prophylactic plasma transfusions will be the domain of the optimistic and the gullible.
The real progress in the field will be made slowly, using carefully defined products either directly isolated from blood or targeting a bloodborne factor, just as the serious pharma and biotech players are doing already.
The field will progress in stages, first creating and carefully testing treatments for well-defined diseases, and only then will it progress to large-scale clinical trials in relatively healthy people to look for the prevention of disease. Most of us will choose to wait for this second stage of trials before undergoing any new treatments. Until then, prophylactic plasma transfusions will be the domain of the optimistic and the gullible.