Evidence reversals in primary care research: a study of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Evidence-Based Medicine is built on the premise that clinicians can be more confident when their decisions are grounded in high-quality evidence. Furthermore, evidence from studies involving patient-oriented outcomes is preferred when making decisions about tests or treatments. Ideally, the findings of relevant and valid trials should be stable over time, that is, unlikely to be reversed in subsequent research. OBJECTIVE: To evaluate the stability of evidence from trials relevant to primary healthcare and to identify study characteristics associated with their reversal. METHODS: We studied synopses of randomized controlled trials (RCTs) published from 2002 to 2005 as "Daily POEMs" (Patient Oriented Evidence that Matters). The initial evidence (E1) from these POEMs (2002-2005) was compared with the updated evidence (E2) on that same topic in a summary resource (DynaMed 2019). Two physician-raters independently categorized each POEM-RCT as (i) reversed when E1 ≠ E2, or as (ii) not reversed, when E1 = E2. For all "Evidence Reversals" (E1 ≠ E2), we assessed the direction of change in the evidence. RESULTS: We evaluated 408 POEMs on RCTs. Of those, 35 (9%; 95% confidence interval [6-12]) were identified as reversed, 359 (88%) were identified as not reversed, and 14 (3%) were indeterminate. On average, this represents about 2 evidence reversals per annum for POEMs about RCTs. CONCLUSIONS: Over 12-17 years, 9% of RCTs summarized as POEMs are reversed. Information alerting services that apply strict criteria for relevance and validity of clinical information are likely to identify RCTs whose findings are stable over time.
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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 | 0.658 | 0.855 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.025 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".