The Fragility of Landmark Randomized Controlled Trials in the Plastic Surgery Literature
Bibliographic record
Abstract
Background: Randomized controlled trials (RCTs) are integral to the progress of evidenced-based medicine and help guide changes in the standards of care. Although results are traditionally evaluated according to their corresponding P value, the universal utility of this statistical metric has been called into question. The fragility index (FI) has been developed as an adjunct method to provide additional statistical perspective. In this study, we aimed to determine the fragility of 25 highly cited RCTs in the plastic surgery literature. Methods: A PubMed search was used to identify the 25 highest cited RCTs with statistically significant dichotomous outcomes across 24 plastic surgery journals. Article characteristics were extracted, and the FI of each article was calculated. Additionally, Altmetric scores were determined for each study to determine article attention across internet platforms. Results: The median FI score across included studies was 4 (2–7.5, interquartile range). The two highest FI scores were 208 and 58, respectively. Four studies (16%) had scores of 0 or 1. Three studies (12%) had scores of 2. All other studies (72%) had FI scores of 3 or higher. The median Altmetric score was 0 (0–3). Conclusion: The FI can provide additional perspective on the robustness of study results, but like the P value, it should be interpreted in the greater context of other study elements.
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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 | 0.569 | 0.865 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".