By Jon Brock
The article below details the reasons why we need to get data right; and even more so with metadata. Because the combined effect of compiling (bad or inadequate) data does not make it more reliable; it likely compounds the error.
This is particularly problematic in scientific academy within the areas of medicine and nutrition research, as John Ioannidis has clearly exposed in his work.
Scientific data can go “absent without leave” for a number of different reasons:
- Scientists don’t archive their data properly and they lose track of it, can’t make sense of it, or their hard-drive dies and they don’t have a back-up. This happens surprisingly (and embarrassingly) often.
- Scientists begin a study but abandon it before it is completed due to lack of funds, unpromising preliminary results, or other priorities. The data might be useful in combination with data from other studies, but it’s not publishable on its own.
- Scientists selectively publish data that supports a particular theory. Inconvenient data are quietly forgotten.
- Scientists try and publish data but are unsuccessful because the results aren’t considered interesting enough by the scientific journals.
- Knowing how difficult it will be to publish a null result, scientists prioritise writing up studies that gave them more publishable results.
The end result is what’s become known as the “file drawer problem”. The published scientific literature represents only a small and biased sample of the research that has actually been conducted. The rest is stuffed away at the back of the metaphorical filing cabinet.
There’s a lot of wasted effort here — data collected and then not used. But the bigger problem is the bias in what is published.
The drug is currently approved for malaria and also for rheumatoid arthritis and systemic lupus erythematosus, which is its main use in the U.S. It’s therefore available to be prescribed off-label, and some clinicians have already said they’re using it on COVID-19 patients. But neither Hahn nor other task force members addressed whether enough hydroxychloroquine is on hand to treat large numbers of coronavirus cases. Convalescent plasma is another treatment the FDA is considering for COVID-19, said FDA Commissioner Stephen Hahn, MD.
Convalescent plasma and the immune globulin that it contains is another possible treatment the agency is considering, Hahn added. “FDA’s been working for some time on this,” he said. “If you’ve been exposed to coronavirus and you’re better — you don’t have the virus in your blood — we could collect the blood, concentrate that and have the ability, once it’s pathogen-free, to give that to other patients, and the immune response could potentially provide a benefit to patients. That’s another thing we’re looking at; over the next couple of weeks, we’ll have information and we’re really pushing hard to try to accelerate that.” Such treatments have been effective in Ebola, for example.
Source: FDA to Study Hydroxychloroquine for COVID-19 | MedPage Today
Jennifer E. DeVoe, MD, DPhil
My teaching session with the medical student at the end of the day included a discussion about patient care decisions and recommendations that go beyond ticking quality boxes and following the latest guidelines. Initially, I felt as if I was rationalizing my delivery of suboptimal care and began to doubt myself.
However, the quality reports I receive each month do not capture the complexity of many patients’ lives.4 These reports fail to reflect the individualized and shared decisions made between a patient and her physician who have known each other for 15 years; the proprietary quality score calculation formulas do not adjust for the healing power of relationships.5 Amid the mounting evidence that primary care saves lives,6 our health care system does not (yet) have a population health analytics tool that captures and tracks the progress that she and I have made together in more than a decade. When will we create better systems with capabilities to measure the emergency department visits that were prevented, the stable housing that was obtained, the increased resiliency she has built into her life, her feelings of empowerment to be a better parent, the reduction in her self-destructive behaviors, and the trusting relationship we have built over time?
Insightful and fascinating glimpse into how medical science has been, and continues to be, distorted by special interests and ideological cabals to the detriment of patients.
“The benefits of carbohydrate restriction in diabetes are immediate and well documented. Concerns about the efficacy and safety are long term and conjectural rather than data driven.
Dietary carbohydrate restriction reliably reduces high blood glucose, does not require weight loss (although is still best for weight loss), and leads to the reduction or elimination of medication. It has never shown side effects comparable with those seen in many drugs.
Here we present 12 points of evidence supporting the use of low-carbohydrate diets as the first approach to treating type 2 diabetes and as the most effective adjunct to pharmacology in type 1. They represent the best-documented, least controversial results. The insistence on long-term randomized controlled trials as the only kind of data that will be accepted is without precedent in science.
The seriousness of diabetes requires that we evaluate all of the evidence that is available. The 12 points are sufficiently compelling that we feel that the burden of proof rests with those who are opposed.”
Our call to retire statistical significance and to use confidence intervals as compatibility intervals is not a panacea. Although it will eliminate many bad practices, it could well introduce new ones. Thus, monitoring the literature for statistical abuses should be an ongoing priority for the scientific community. But eradicating categorization will help to halt overconfident claims, unwarranted declarations of ‘no difference’ and absurd statements about ‘replication failure’ when the results from the original and replication studies are highly compatible. The misuse of statistical significance has done much harm to the scientific community and those who rely on scientific advice. Pvalues, intervals and other statistical measures all have their place, but it’s time for statistical significance to go.
Great insight and perspective by Heath McAnally, MD, MSPH, regarding the sometimes reactive, albeit good intentioned, response of gov’t and private entities to the opioid crisis. Worth the read for sure.
Pendulum swings in medicine aren’t new, but damping the oscillation rarely bears such urgency. To paraphrase the original document, we call on our leaders to:
Recognize that opioid tapering requires evidence-based careful selection, patient-centered methods, realistic goals, and close monitoring for adverse events.
Include the expertise of pain management subspecialists at every level of decision-making about future opioid policies and guidelines.
Put a halt to policies forcing opioid tapering/cessation outside the contexts of diversion or unequivocal, documented harm: benefit ratio imbalance
Dr. McAnally is a board-certified anesthesiologist, pain physician, and addictionologist practicing in Alaska (the military sent him there and he decided to stay). If he wasn’t trying to guide people in improving their own lives, teaching medical students to do the same, or writing about it, he’d probably be outdoors right now slogging up a mountain with a good friend or two.
Source: Opioid Policy: The Devil and the Deep Blue Sea | Medpage Today