Strength in numbers? The fragility index of studies from the Scandinavian knee ligament registries
Bibliographic record
Abstract
PURPOSE: The fragility index (FI) is a metric to evaluate the robustness of statistically significant results. It describes the number of patients who would need to change from a non-event to an event to change a result from significant to non-significant. This systematic survey aimed to evaluate the feasibility of applying the FI to findings related to anterior cruciate ligament (ACL) reconstruction in the Scandinavian knee ligament registries. METHODS: The PubMed, EMBASE, Cochrane Library and AMED databases were searched. Studies from the Scandinavian knee ligament registers were eligible if they reported a statistically significant result (p < 0.05) for any of the following dichotomous outcomes; ACL revision, contralateral ACL reconstruction or the presence of postoperative knee laxity. Only studies with a two-arm comparative analysis were included. Eligibility assessment, data extraction and quality assessment were performed by two independent reviewers. The dichotomous analyses were stratified according to the grouping variable for the two comparative arms as follows; age, patient sex, activity at injury, graft choice, drilling technique, graft fixation, single- versus double-bundle, concomitant cartilage injury and country. The two-sided Fisher's exact test was used to calculate the FI of all statistically significant analyses. RESULTS: From 158 identified studies, 13 studies were included. They reported statistical significance for a total of 56 dichotomous analyses, of which all but two had been determined by a time-to-event analysis. The median sample size for the arms was 5540 (range 92-38,666). The mean FI for all 56 dichotomous analyses was 80.6 (median 34.5), which means that a mean of 80.6 patients were needed to change outcome status to generate a non-significant result instead of a significant one. Seventeen analyses (30.4%) immediately became non-significant when performing the two-sided Fisher's exact test and, therefore, had an FI of 0. The analyses related to age were the most robust, with a mean FI of 178.5 (median 116, range 1-1089). The mean FI of the other grouping variables ranged from 0.5 to 48.0. CONCLUSION: There was large variability in the FI in analyses from the Scandinavian knee ligament registries and almost one third of the analyses had an FI of zero. The FI is a rough measurement of robustness when applied to registry studies, however, future studies are needed to determine the most appropriate metric for robustness in registry studies. The use of the FI can provide clinicians with a deeper understanding of significant study results and promotes an evidence-based approach in the clinical care of patients. LEVEL OF EVIDENCE: Systematic review of prospective cohort studies, Level II.
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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 | 0.145 | 0.457 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".