The continuous fragility index of outcomes in rotator cuff repair augmentation randomized trials: a systematic review
Bibliographic record
Abstract
BACKGROUND: Symptomatic rotator cuff tears often undergo surgical repair, which may be paired with various augmentation strategies to enhance structural healing rates. While many randomized controlled trials (RCTs) evaluate augmentation techniques, the statistical robustness of many findings in these studies is unknown. This systematic review aims to evaluate the continuous fragility index (CFI) of RCTs on augmentation techniques for rotator cuff repairs. METHODS: MEDLINE, Embase, and CENTRAL databases were comprehensively searched from inception to September 2023 for RCTs assessing the efficacy of at least 1 augmentation strategy during rotator cuff repair. Eligible studies reported at least 1 statistically significant finding for a continuous outcome. The CFI for eligible outcomes was calculated, with median CFI presented by type of augmentation and outcome. Multivariable regression was performed to identify associations between CFI and other outcome variables. RESULTS: Nineteen RCTs (1305 patients) were included in the final analysis. The median CFI for the 86 outcomes analyzed was 5.85 (interquartile range [IQR]: 2.3-14.4). Augmentation-specific analysis demonstrated variability in CFIs, with the most robust outcomes found in platelet-rich plasma studies (median: 10.95; IQR: 3.3-19.0) and suture-spanning augmentation studies (median: 11.90; IQR: 11.45-14.35). Outcome-specific analysis demonstrated range of motion outcomes as most robust (median: 9.85; IQR: 7.58-14.0) and strength-related outcomes as most fragile (median: 2.00; IQR: 1.0-16.3). Multivariable regression identified larger sample size as a statistically significant predictor of greater CFI. Notably, loss to follow-up exceeded the CFI in 31.4% of outcomes. CONCLUSION: The observed median CFI of 5.85 in augmentation trials is consistent with the CFI reported in orthopedic and sports medicine literature. However, almost a third of outcomes had a loss to follow-up exceeding their CFI, risking the reversal of study findings with more robust follow-up and outcomes. Clinicians and researchers should consider fragility in addition to P values when assessing study results, especially in the context of high loss to follow-up. Future trials should report the fragility of their findings.
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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 | 0.035 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.019 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".