Perceptions and Competence in Evidence-Based Medicine
Bibliographic record
Abstract
Background:The Journal of Bone and Joint Surgery, American Volume (The Journal) recently initiated a section called “Evidence-Based Orthopaedics.” Furthermore, a level-of-evidence rating is now used in The Journal to help readers in clinical decision-making. Little is known about whether this recent emphasis has influenced surgeons' perceptions about and competence in evidence-based medicine. Therefore, we examined perceptions and competence in evidence-based medicine among Dutch orthopaedic surgeons. Methods: Members of the Dutch Orthopaedic Association were surveyed to examine their attitudes toward evidence-based medicine and their competence in evidence-based medicine. We evaluated competences using a newly developed instrument tailored to surgical practice. Results: Of the 611 members, 367 surgeons (60%) responded. Orthopaedic surgeons welcomed evidence-based medicine. Practical evidence-based medicine resources were perceived as the best method to move from opinion-based or experience-based to evidence-based practice. Four variables were significantly and positively associated with the competence instrument: (1) a younger age, particularly between thirty-six and forty-five years (p = 0.007), (2) experience of less than ten years (p = 0.032), (3) having a PhD degree (p < 0.001), and (4) working in an academic or teaching setting (p = 0.004). The majority of the respondents were aware of The Journal's evidence-based medicine section (84%) and level-of-evidence ratings (65%), and 20% used The Journal's evidence-based medicine abstracts in clinical decision-making. This increased awareness of evidence-based medicine was also reflected in the frequent use of Cochrane reviews in clinical decision-making (27% of the respondents). Surgeons who used and those who were aware of but did not use The Journal's evidence-based medicine abstracts or Cochrane reviews in clinical decision-making had significantly higher competence instrument scores than those who were unaware of these resources (p = 0.03 and p < 0.001, respectively). Conclusions: Evidence-based medicine is welcomed by Dutch orthopaedic surgeons. The recent emphasis on evidence-based medicine is reflected in an increased awareness about The Journal's evidence-based medicine section, levels of evidence, and the largest evidence-based medicine resource: the Cochrane reviews. Younger orthopaedic surgeons had better knowledge about evidence-based medicine. The development and use of evidence-based resources as well as preappraised summaries such as The Journal's evidence-based medicine abstracts and Cochrane reviews were perceived as the best way to move from opinion-based to evidence-based orthopaedic practice.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".