Predictors of Increased Fragility Index Scores in Surgical Randomized Controlled Trials: An Umbrella Review
Bibliographic record
Abstract
BACKGROUND: The fragility index (FI) is defined as the minimum number of patients or subjects needed to switch experimental groups for statistical significance to be lost in a randomized control trial (RCT). This index is used to determine the robustness of a study's findings and recently as a measure of evaluating RCT quality. The objective of this review was to identify and describe published systematic reviews utilizing FI to evaluate surgical RCTs and to determine if there were common factors associated with higher FI values. METHODS: Three databases (PubMed, MEDLINE [Ovid], Embase) were searched, followed by a subsequent abstract/title and full-text screening to yield 50 reviews of surgical RCTs. Authors, year of publication, name of journal, study design, number of RCTs, subspecialty, sample size, median FI, patients lost to follow-up, and associations between variables and FI scores were collected. RESULTS: Among 1007 of 2214 RCTs in 50 reviews reporting FI (median sample size 100), the pooled median FI was 3 (IQR: 1-7). Most reviews investigated orthopaedic surgery RCTs (n = 32). There was a moderate correlation between FI and p value (r = 0.-413), a mild correlation between FI and sample size (r = 0.188), and a mild correlation between FI and event number (r = 0.129). CONCLUSION: Based on a limited sample of systematic reviews, surgical RCT FI values are still low (2-5). Future RCTs in surgery require improvement to study design in order to increase the robustness of statistically significant 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 | 0.114 | 0.408 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".