McMaster PLUS: A Cluster Randomized Clinical Trial of an Intervention to Accelerate Clinical Use of Evidence-based Information from Digital Libraries
Bibliographic record
Abstract
BACKGROUND: Physicians have difficulty keeping up with new evidence from medical research. METHODS: We developed the McMaster Premium LiteratUre Service (PLUS), an internet-based addition to an existing digital library, which delivered quality- and relevance-rated medical literature to physicians, matched to their clinical disciplines. We evaluated PLUS in a cluster-randomized trial of 203 participating physicians in Northern Ontario, comparing a Full-Serve version (that included alerts to new articles and a cumulative database of alerts) with a Self-Serve version (that included a passive guide to evidence-based literature). Utilization of the service was the primary trial end-point. RESULTS: Mean logins to the library rose by 0.77 logins/month/user (95% CI 0.43, 1.11) in the Full-Serve group compared with the Self-Serve group. The proportion of Full-Serve participants who utilized the service during each month of the study period showed a sustained increase during the intervention period, with a relative increase of 57% (95% CI 12, 123) compared with the Self-Serve group. There were no differences in these proportions during the baseline period, and following the crossover of the Self-Serve group to Full-Serve, the Self-Serve group's usage became indistinguishable from that of the Full-Serve group (relative difference 4.4 (95% CI -23.7, 43.0). Also during the intervention and crossover periods, measures of self-reported usefulness did not show a difference between the 2 groups. CONCLUSION: A quality- and relevance-rated online literature service increased the utilization of evidence-based information from a digital library by practicing physicians.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".