The Influence of Large Clinical Trials in Orthopedic Trauma
Bibliographic record
Abstract
OBJECTIVES: To evaluate the influence of top fracture trials on the practice of orthopedic surgeons. DESIGN: This is a cross-sectional study. PARTICIPANTS: We electronically administered the survey to all members of the Canadian Orthopedic Association. We received responses for 222 surveys, of which, 178 surveys were completed. INTERVENTION: We distributed a survey that evaluated the influence of 7 important fracture studies (6 randomized controlled trials and 1 prospective cohort study) on practice, patient care and the overall advancement of knowledge in the field of orthopedics. This study was approved by our local ethics review board. MAIN OUTCOME MEASURE: The primary outcome measure was the perceived general influence and impact of important fracture studies on the perceptions and practice of orthopedic surgeons. RESULTS: The Clavicular Fixation Trial (2007) and Tibial Fracture Trial (SPRINT, 2008) were perceived by surgeons to have the greatest influence on advancing overall knowledge in the field, improving personal practice, and the most influence on improving patient care. On the other hand, the Bone Stimulation in Fractures Trial (2011) and the recombinant human bone morphogenetic protein-2-BESST Trial (2002) had the lowest mean influence ranks. The probability of changing practice was significantly higher (Odds Ratio, 2.89; 95% confidence interval, 2.16-3.88; P < 0.00001) when studies had positive outcomes in comparison with negative outcomes. CONCLUSIONS: Despite the complexity and costs associated with clinical trials in orthopedic trauma, the results from this survey suggest that these studies result in a demonstrable perceived influence and impact on the practice of orthopedic surgeons.
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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 | 0.261 | 0.651 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".