Bibliographic record
Abstract
Background: Evidence based medicine (EBM) paradigm places systematic reviews and metaanalyses,ideally of randomized clinical trials (RCTs), at the top of the evidential pyramid.However, resolving situations with “conflicting” or “missing” evidence can be problematic.Methods: This is a case-based review of Bayesian techniques to assist in optimizing the interpretationof well performed RCTs with conflicting evidence. Using the example of cochicinein post acute myocardial infarction subjects, it is demonstrated how these techniques avoidcommon interpretative cognitive biases, provide additional analytical nuances, and therebyenhancing the original published conclusions.Results: A previous RCT (n=4745) had claimed a reduction in cardiovascular (CV) withcolchicine (p=0.02). A more recent and larger RCT (n=7062) concluded that colchicine “didnot reduce the incidence of CV events (p=0.93)”. This Bayesian analysis suggests that theeffect of colchicine is indeterminate with a probability of a clinically meaningful benefit, definedas a 10% reduction in the relative risk, that varies between 13% and 41% depending onwhether the earlier study is ignored or considered.Conclusions: If the trials are of equal high quality, conflicts are often illusory arising from theimproper comparisons of statistical significance. Current statistical approaches which ignoreprior evidence and rely on null hypothesis significance testing lead to vacillating beliefs thatdo not always faithfully respect the laws of probability and consequently may not align withthe true state of knowledge. Bayesian techniques can address these issues and raise the qualityof clinical trial interpretations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.409 | 0.614 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".