Daily versus weekly evidence reports for orthopaedic surgeons in India
Bibliographic record
Abstract
BACKGROUND: There is a dearth of research regarding the impact of evidence-based medicine (EBM) tools, such as evidence summaries, in developing countries. The goals of this study were to: investigate accessibility, use, and impact of an online EBM knowledge dissemination portal in orthopaedic surgery in India; explore whether receiving daily targeted evidence summaries results in more frequent use of an EBM tool compared with receiving general weekly reports; and identify and explain the barriers and benefits of an online EBM resource in the Indian context. METHODS: Forty-four orthopaedic surgeons in Pune, India, were provided free access to OrthoEvidence (OE), a for-profit, online EBM knowledge dissemination portal. Participants were subsequently randomized into 2 groups-1 group received daily targeted evidence summaries while the other received general weekly summaries. This study employed an explanatory sequential mixed methods design that incorporated 2 questionnaires, OE usage data, and semi-structured interviews to gain insight into the surgeons' usage, perceptions, and impact of OE. RESULTS: There were no observable differences in OE usage between groups. OE was deemed to be comprehensive, practical, useful, and applicable to clinical practice by the majority of surgeons. The exit survey data revealed no differences between groups' perceptions of the OE tool. semi-structured interviews revealed barriers to keeping up with evidence that included limited access to relevant medical literature and limited incentive to keep up with current evidence. CONCLUSIONS: Neither frequency of delivery (daily versus weekly) nor targeted versus general content affected the use of evidence summaries. Facilitating uptake of current evidence into clinical practice among Indian orthopedic surgeons may require additional components beyond dissemination of evidence summaries.
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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 | 0.016 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".