Predicting the Probability That Open-Access Clinical Literature Saves Lives
Bibliographic record
Abstract
Whether open-access (OA) clinical literature directly saves lives is frequently debated, yet empirical documentation is scarce because clinical notes rarely record how evidence was accessed. This study synthesizes high-impact cases of OA-enabled clinical change—most notably the SARS-CoV-2 PCR diagnostic protocol and the RECOVERY dexamethasone findings—and develops an expanded Bayesian predictive model estimating the probability that a single clinician reading one OA article saves a life. We integrate three primary evidence bases: (1) clinician-reported rates of practice change following article consultation, (2) the proportion of clinical decisions that influence short- or long-term mortality, and (3) empirically observed mortality reductions following OA-mediated dissemination of life-saving therapeutic evidence. We then extend this model by incorporating additional determinants of diagnostic and therapeutic accuracy, including medical error rates, years of clinical experience, multimorbidity-dependent diagnostic entropy, cognitive load, structural barriers, team-based reliability, guideline adherence, and electronic health record (EHR)–related error susceptibility, formalized in a multilevel Bayesian framework. The core model yields a probability range of p ≈ 0.003–0.02 that a clinician–article encounter prevents one death, corresponding to a Number Needed to Treat (NNT) analog of approximately 50–330 clinician–article encounters. After accounting for heterogeneity in clinical acuity, multimorbidity, and the extended set of clinician and system parameters, hierarchical Bayesian extensions adjust the predictive interval to p ≈ 0.002–0.03 and NNT ≈ 30–500. The integrated analysis demonstrates that OA literature meaningfully increases the probability of life-saving clinical decisions, especially in high-acuity environments where marginal improvements in evidence latency and accuracy have large mortality consequences.
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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 | 0.048 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".