High continuous fragility index values amongst trials comparing anterior cruciate ligament reconstruction with and without anterolateral complex procedures: A systematic review
Bibliographic record
Abstract
BACKGROUND: This systematic review aimed to determine the statistical fragility of randomized controlled trials (RCTs) comparing anterior cruciate ligament reconstruction (ACLR) with or without anterolateral complex (ALC) procedures. METHODS: PubMed, MEDLINE, and EMBASE were searched from inception to April 22, 2024 for RCTs comparing ACLR with or without ALC procedures (lateral extra-articular tenodesis or anterolateral ligament reconstruction). Studies that reported ≥1 statistically significant continuous outcome, statistically significant dichotomous outcome, and/or non-significant dichotomous outcome were included. The fragility index (FI), continuous fragility index (CFI), and reverse fragility index (RFI) were calculated for these outcomes, respectively. RESULTS: 20 RCTs including 2,292 patients were included (mean study size: 114.6 patients). The median FI across 21 outcomes from 11 studies was 2.0 (interquartile range [IQR], 2.0). The median CFI across 20 outcomes from seven studies was 16.9 (IQR, 227.7). The median RFI across 107 outcomes from 19 studies was 5.0 (IQR, 2.0). The number of patients lost to follow-up at the final follow-up period was more than the study-specific FI in eight (72.7%) studies, CFI in two (28.6%) studies, and RFI in 12 (63.2%) studies. CONCLUSION: This systematic review demonstrated that RCTs comparing ACLR with or without ALC procedures have low FI and RFI values that tended to exceed loss to follow-up, demonstrating relative statistical fragility of existing literature. However, CFI values were high amongst RCTs, suggesting robustness in findings for quantitative outcomes. While fragility indices are important metrics of robustness to consider, their application in research and clinical practice should be further investigated.
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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 | 0.051 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".