Assessing the fragility index of randomized controlled trials supporting perioperative care guidelines: A methodological survey protocol
Bibliographic record
Abstract
INTRODUCTION: The Fragility Index (FI) and the FI family are statistical tools that measure the robustness of randomized controlled trials (RCT) by examining how many patients would need a different outcome to change the statistical significance of the main results of a trial. These tools have recently gained popularity in assessing the robustness or fragility of clinical trials in many clinical areas and analyzing the strength of the trial outcomes underpinning guideline recommendations. However, it has not been applied to perioperative care Clinical Practice Guidelines (CPG). OBJECTIVES: This study aims to survey clinical practice guidelines in anesthesiology to determine the Fragility Index of RCTs supporting the recommendations, and to explore trial characteristics associated with fragility. METHODS AND ANALYSIS: A methodological survey will be conducted using the targeted population of RCT referenced in the recommendations of the CPG of the North American and European societies from 2012 to 2022. FI will be assessed for statistically significant and non-significant trial results. A Poisson regression analysis will be used to explore factors associated with fragility. DISCUSSION: This methodological survey aims to estimate the Fragility Index of RCTs supporting perioperative care guidelines published by North American and European societies of anesthesiology between 2012 and 2022. The results of this study will inform the methodological quality of RCTs included in perioperative care guidelines and identify areas for improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.434 | 0.512 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".