Fragility index for extended prophylaxis following abdominopelvic surgery: A methodological survey
Bibliographic record
Abstract
BACKGROUND: Fragility Index (FI) is increasingly used to assess robustness of statistically significant p-values reported in randomized controlled trials (RCTs). FI represents the lowest number of non-events changed to events that would make study findings non-significant. This methodological survey was designed to assess the fragility of the evidence for extended VTEp following major abdominopelvic surgery. METHODS: MEDLINE, Embase, and CENTRAL were searched from inception to November 2023. RCTs with parallel, double-armed, superiority design comparing extended VTEp for patients undergoing major abdominopelvic surgery to controls with at least one statistically significant dichotomous outcome were included. Walsh et al.'s method of calculating FI was utilized. RESULTS: After review of 611 citations, 6 RCTs were identified with 12 statistically significant outcomes between groups. The mean number of patients randomized per RCT was 419 (SD 176). The median FI was 1.5 (range: 1-4). The number of patients lost to follow-up was greater than the FI for 10/12 (83.3 %) outcomes. CONCLUSIONS: Statistically significant differences reported in RCTs evaluating extended VTEp following major abdominopelvic surgery are not robust.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".