The Fragility of Statistically Significant Findings From Randomized Trials in Sports Surgery: A Systematic Survey
Bibliographic record
Abstract
BACKGROUND: High-quality, evidence-based orthopaedic care relies on the generation and translation of robust research evidence. The Fragility Index is a novel method for evaluating the robustness of statistically significant findings from randomized controlled trials (RCTs). It is defined as the minimum number of patients in 1 arm of a trial that would have to change status from a nonevent to an event to alter the results of the trial from statistically significant to nonsignificant. PURPOSE: To calculate the Fragility Index of statistically significant results from clinical trials in sports medicine and arthroscopic surgery to characterize the robustness of the RCTs in these fields. METHODS: A search was conducted in Medline, EMBASE, and PubMed for RCTs related to sports medicine and arthroscopic surgery from January 1, 2005, to October 30, 2015. Two reviewers independently assessed titles and abstracts for study eligibility, performed data extraction, and assessed risk of bias. The Fragility Index was calculated using the Fisher exact test for all statistically significant dichotomous outcomes from parallel-group RCTs. Bivariate correlation was performed to evaluate associations between the Fragility Index and trial characteristics. RESULTS: A total of 48 RCTs were included. The median sample size was 64 (interquartile range [IQR], 48.5-89.5), and the median total number of outcome events was 19 (IQR, 10-27). The median Fragility Index was 2 (IQR, 1-2.8), meaning that changing 2 patients from a nonevent to an event in the treatment arm changed the result to a statistically nonsignificant result, or P ≥ .05. CONCLUSION: Most statistically significant RCTs in sports medicine and arthroscopic surgery are not robust because their statistical significance can be reversed by changing the outcome status on only a few patients in 1 treatment group. Future work is required to determine whether routine reporting of the Fragility Index enhances clinicians' ability to detect trial results that should be viewed cautiously.
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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 | 0.478 | 0.811 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".