Statistical Significance in Trauma Research: Too Unstable to Trust?
Bibliographic record
Abstract
OBJECTIVES: To evaluate the stability of statistical findings in the fracture care literature based on minor changes in event rates and to determine the utility of applying both the Fragility Index (FI) and Fragility Quotient (FQ) to comparative orthopaedic trauma trials. METHODS: All fracture care studies from 1991 to 2013 in the Journal of Bone and Joint Surgery and the Journal of Orthopaedic Trauma were screened. The FI was determined by altering the number of reported outcome events, a single event at time, until a reversal of statistical significance was determined. The associated FQ was determined by dividing the FI by the total sample size. RESULTS: Of the 4040 studies evaluated, 198 comparative studies met inclusion criteria with a reported 253 primary and 522 secondary outcome events. There were 118 randomized controlled trials and 80 retrospective comparative studies. Of the 775 total outcome events, 235 were initially reported as significant. The median FI for the entire study was only 5 with an associated FQ of 0.046. This represents just 3.8% of the total study population. CONCLUSIONS: The robustness of comparative trials in the orthopaedic trauma literature may not be as stable as previously thought with only a few event reversals required to alter trial significance. We therefore recommend triple reporting of a P value, FI, and FQ to aid in the evaluation and interpretation of statistical stability and quantitative significance in comparative orthopaedic trauma trials.
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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 | 0.731 | 0.930 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".