Assessment of unmet needs and the lack of generalizability in the design of randomized controlled trials for scleroderma treatment
Bibliographic record
Abstract
OBJECTIVE: To determine the generalizability of randomized controlled trials (RCTs) in the treatment of systemic sclerosis (SSc) using the Canadian Scleroderma Research Group (CSRG) database. METHODS: We identified articles related to SSc published from 1958 to 2006. Key points on trial design were recorded. The inclusion/exclusion criteria were used in conjunction with the CSRG database to determine the proportion of patients with SSc who would theoretically be eligible for these trials. Articles were classified into subcategories according to the target system. The CSRG database contains 438 patients with SSc from 14 Canadian centers. Results were in median (%) and mean (%) with 95% confidence intervals (95% CIs). RESULTS: In total, 210 articles were evaluated and 73 were selected for inclusion in this study. The mean percentage of eligible patients with SSc associated with other conditions was 35% (95% CI 17-53) for Raynaud's phenomenon, 24% (95% CI 1-47) for digital ulcers, 48% (95% CI 27-68) for gastrointestinal (GI) involvement, 32% (95% CI 20-43) for overall disease modification, 6% (95% CI 4-8) for pulmonary arterial hypertension, 2% (95% CI 0-4) for interstitial lung disease, and 38% (95% CI 12-64) for other categories. CONCLUSION: Except for GI trials, <38% of the identified patients with SSc would have been suitable to enter the RCTs. Although some patients would be ineligible because they lack certain organ involvement, RCTs designed to include appropriate patients with SSc are needed; there are few proven treatments and trials typically do not include the majority of those who could potentially benefit from the intervention.
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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 | 0.861 | 0.939 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| 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".