Quality of randomized clinical trials in juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVES: We evaluated the quality of randomized clinical trials (RCTs) of therapy for juvenile idiopathic arthritis (JIA) using an individual component approach and assessed temporal changes. METHODS: A systematic review of the literature was performed to identify all RCTs involving exclusively JIA patients. Two investigators independently assessed the identified articles for six quality indicators: generation of allocation sequence, allocation concealment, masking, intention-to-treat (ITT) analysis, dropout rates and clearly stated primary outcome. RESULTS: Fifty-two RCTs involving JIA patients were assessed. Generation of allocation sequence was unclear in 79% of the studies. Reporting of allocation concealment was adequate in only one-third of the studies. Masking was adequate in 73%, inadequate in 19% and unclear in 8% of the reports. ITT analysis was employed in 37% of the reports. Per-protocol analysis was used in 40% and in 23% the method was unclear. Most of the reports (67%) had dropout rates < or = 20%. About half of the reports (n = 25) failed to show a significant effect of the experimental treatment. No significant associations were found between the study results and quality indicators. With the exception of adequate masking and dropout rate, all quality indicators showed a trend of improvement over the decades. CONCLUSIONS: The quality of RCTs in JIA based on the selected indicators was poor. Although there were some positive changes over time, the reporting and methodological quality of trials should be improved. New, more powerful and acceptable RCT designs should be developed in this patient population.
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
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 | 0.546 | 0.815 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| 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".