Recurring low statistical robustness in orthopaedic surgery: A systematic review of 84 fragility index studies
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Purpose Increasing attention has been directed towards the fragility index (FI) and reverse fragility index (RFI) in orthopaedic surgery. The purpose of this study was to amalgamate the FI and RFI literature in orthopaedic surgery, and critically appraise its clinical impact. Methods Three databases were searched from inception to 22 March 2025, for articles evaluating either the FI or RFI across orthopaedic surgery. Median and mean FI and RFI were presented as ranges with median values. Findings from correlation analyses assessing the impact of study characteristics on FI were consolidated. Citation analysis was performed to assess the uptake of FI/RFI literature. Results Eighty‐four studies were included in the final analysis. Sports medicine was the most represented subspeciality (25.0%). Median FI of subspeciality‐specific studies ranged from 1 to 6, and RFI from 3 to 7. Median FI of pathology‐specific studies ranged from 0 to 12, and RFI from 2 to 10. The RFI exceeded the FI in most pathology‐specific studies (93.3%). Decreasing p‐value (88%), increasing sample size (50%) and increasing study power (50%) were commonly found to be associated with increasing fragility index. The median number of citations was 11.5 (interquartile range [IQR], 3.0–27.0) with a median citation density of 3.1 (IQR, 1.2–6.2). Sports medicine publications had the highest collective median citation density of 4.3 (IQR, 1.0–6.6). The h‐index for all included studies was 25, indicating 25 studies had at least 25 citations. Earlier publication year ( p < 0.001) and increasing journal impact factor ( p = 0.007) were associated with increased citations. Conclusion The majority of fragility index research is concentrated across a few orthopaedic subspecialties with redundant findings indicating low statistical robustness. Recurring methodologic recommendations based on correlation analyses include increasing patient sample size to increase study power. Methodological recommendations from this body of research should be integrated into future original studies to strengthen statistical robustness. Level of Evidence Level III.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it