Introducing the fragility index—A case study using the Term Breech Trial
Bibliographic record
Abstract
The fragility index (FI) is a sensitivity analysis of the statistically significant result of a clinical study. It is the number of hypothetical changes in the primary event of one of the two cohorts in a 1-to-1 comparative trial to render the statistically significant result non-significant (ie, to alter the P-value from ≤0.05 to >0.05). The FI can be compared with the patient drop-out rates and protocol violations, which, if much higher than the FI, may arguably suggest less robustness/stability of the trial's results. To illustrate the concept, we have chosen the Term Breech Trial (TBT) as a case study. The TBT results favor planned cesarean birth, as opposed to planned vaginal delivery, in the term singleton fetus with breech presentation. Our analysis shows that the FI of the TBT is 21, which is small in comparison to the number (hundreds) of protocol violations present. Some experts have suggested the inclusion of the FI in data analysis and subsequent discussion of clinical trial data. Routine use of such a metric may be valuable in encouraging readers to maintain a healthy degree of skepticism, especially when interpreting trial results which may directly influence clinical practice.
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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 | 0.316 | 0.533 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".