Orthopaedic Surgical Content Associated with Resources for Clinical Evidence
Bibliographic record
Abstract
Introduction: As evidence-based medicine is increasingly being adopted in medical and surgical practice, effective processing and interpretation of medical literature is imperative. Databases presenting the contents of medical literature have been developed; however, their efficacy merits investigation. The objective of this study was to quantify surgical and orthopaedic content within five evidence-based medicine resources: DynaMed, Clinical Evidence, UpToDate, PIER, and First Consult. Methods: We abstracted surgical and orthopaedic content from UpToDate, DynaMed, PIER, First Consult, and Clinical Evidence. We defined surgical content as that which involved surgical interventions. We classified surgical content by specialty and, for orthopaedics, by subspecialty. The amount of surgical content, as measured by the number of relevant reviews, was compared with the total number of reviews in each database. Likewise, the amount of orthopaedic content, as measured by the number of relevant reviews, was compared with the total number of reviews and the total number of surgical reviews in each database. Results: Across all databases containing a total of 13268 reviews, we identified an average of 18% surgical content. Specifically, First Consult and PIER contained 28% surgical content as a percentage of the total database content. DynaMed contained 14% and Clinical Evidence 11%, whereas UpToDate contained only 9.5% surgical content. Overall, general surgery, pediatrics, and oncology were the most common specialty areas in all databases. Discussion: Our findings suggest that the limited surgical content within these large scope resources poses difficulties for physicians and surgeons seeking answers to complex clinical questions, specifically within the field of orthopaedics. This study therefore demonstrates the potential need for, and benefit of, surgery-specific or even specialty-specific tools.
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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 | 0.020 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".