Assessment of the Methodologic Quality of Medical and Surgical Clinical Trials in Patients with Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To assess the methodological quality of randomized controlled trials (RCT) of medical and surgical therapy in patients with arthroplasty. METHODS: We conducted a Medline database search for all arthroplasty RCT from 1997 and 2006. The quality of the methods of all eligible RCT was assessed by a trained abstractor. We used a checklist of trial quality characteristics, and the overall trial quality was assessed by 3 scales: Jadad (range 0-5), Delphi list (range 0-9), and numeric rating scale (NRS; range 1-10), based on User's Guides to the Medical Literature. RESULTS: A total of 196 articles were included in the analysis; most included hip (n = 81) or knee (n = 80) or both hip/knee arthroplasty (n = 19); 66 (34%) assessed pharmacological treatments, 117 (60%) nonpharmacological treatments, and 13 (7%) both. Mean (SEM) overall quality scores of arthroplasty RCT were low: Jadad score 2.36 (1.4), Delphi list 5.33 (1.6), and NRS score 4.30 (2.6). Multivariable analyses revealed that nonpharmacological intervention RCT had lower odds (odds ratio 0.28-0.39; p = 0.008-0.033) and those with no funding had lower odds (OR 0.28-0.50; p = 0.014-0.119) of being in the highest quartiles of the 3 overall quality scores. In contrast, multicenter RCT had 1.8-4.7 times higher odds of being in highest tertiles of quality scores (p = 0.017-0.185). CONCLUSION: Methodological deficiencies in reporting of hip/knee arthroplasty RCT offer an opportunity for improvement. Type of intervention, number of trial centers, and presence of funding were independently associated with overall trial quality. In future, multicenter RCT (rather than single-center) and modeling protocols of single-center RCT similar in rigor to multicenter RCT may improve the quality of arthroplasty RCT.
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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 | 0.735 | 0.881 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".