Systematic evaluation of evidence-based orthopedic definitions and visual analysis of evidence-based orthopedic literature
Bibliographic record
Abstract
Objective Systematically search the international and national literature on evidence-based orthopaedics to evaluate the definition of evidence-based orthopaedics and analyze the current state of research.Methods PubMed, Web of Science, EMbase, CNKI, VIP, CBM databases, and Baidu, Google were searched by computer. Evidence-based orthopedic related literature was included to extract evidence-based orthopedic definitions and related information, and Citespace software was used to analyze the literature.Results In terms of evidence-based orthopaedic definitions, a total of 6 definitions of evi-dence-based orthopedics were obtained from 1 Chinese literature, 3 English literature, 1 Chinese book, and 1 English book. In terms of evidence-based orthopaedic literature, the number of articles published in Chinese (462) and English (583) showed a trend of in-creasing and then decreasing with the year; Prof. Bhandari, Mohit is the most prominent expert in this field. The most published institution in China is the West China Hospital, Sichuan University and the foreign institutions are Harvard University, University of Toronto and McMaster University. The United States and Canada are the leaders in this field. Most of the Chinese studies are focused on evidence-based care, clinical teaching and treatment; most of the international studies are focused on follow up and treatment.Conclusion Evidence-based orthopedics definitions vary widely and cannot express the connotations completely and accurately. Research on evidence-based orthopaedics is in-adequate, and there is a need to strengthen theoretical research on evidence-based or-thopaedics and evidence-based research in the field of orthopedics.
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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 | 0.074 | 0.298 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.011 |
| Bibliometrics | 0.092 | 0.038 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".