Assessing the statistical fragility of randomized controlled trials in hip and knee arthroplasty: A methodological review
Bibliographic record
Abstract
Introduction: Randomized controlled trials (RCTs) are considered the gold standard in evidence-based medicine, providing high-quality evidence for the effectiveness of interventions in healthcare. However, the quality of RCTs can vary substantially. One aspect of methodological quality that has recently garnered interest is the fragility index (FI) which is a metric indicating how many event changes would lead to a change the significance of a study's results. Surgical RCTs, especially in orthopedic fields like hip and knee arthroplasty, have been shown to have high fragility, raising concerns about their reliability. This methodological study aims to describe the statistical fragility of RCTs in hip and knee arthroplasty over the past decade, with a secondary objective of determining the study characteristics associated with fragility. Methods: We conducted a systematic search of Medline and Embase databases for RCTs published between 2012 and 2022, focusing on hip and knee arthroplasty. Trials were included if they had a 1:1 parallel design and reported at least one statistically significant outcome. FI were calculated for both dichotomous and continuous outcomes using established methods. We extracted data such as sample size, study characteristics, and statistical measures. Multivariable regression was used to explore relationships between FI and study characteristics such as sample size, intervention type, and region. Results: From 16,214 records, 140 studies met the inclusion criteria. The median FI for dichotomous outcomes was 2, interquartile range (IQR) = 4, while the median continuous FI (CFI) was 8.85 (IQR 14.4), indicating higher robustness for continuous outcomes. No significant associations were found between FI and variables like region, year of publication, or sample size. Conclusions: Hip and knee arthroplasty trials often exhibit statistical fragility, particularly those reporting dichotomous outcomes. These fragile findings suggest the need for more robust RCT designs in orthopedic research. Incorporating FI into sample size calculations could improve trial stability and ensure more reliable outcomes that better inform clinical guidelines and patient care.
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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 | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.379 | 0.749 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.033 | 0.033 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".