The Fine Line between Success and Failure in Scleroderma Lung Fibrosis Trials
Bibliographic record
Abstract
The fine line between success and failure in scleroderma lung fibrosis trials Scleroderma (systemic sclerosis; SSc) is a complex multisystem disease with high morbidity and mortality.Pulmonary complications are a major cause of death in SSc; however, there are few effective treatments of lung fibrosis (interstitial lung disease; ILD).The evidence base for the current approach of using broad-spectrum immunosuppression with mycophenolate mofetil or cyclophosphamide in SSc-ILD has been supported by three prospective randomized trials, but in general these trials are difficult to design and execute.This is primarily due to SSc-ILD being a rare disease with a relatively slow rate of progression compared with idiopathic pulmonary fibrosis (IPF).This slower progression is reassuring for patients, but makes it more challenging to adequately power clinical trials.However, despite these challenges, it is important to undertake studies given the substantial impact of pulmonary fibrosis on morbidity and mortality in SSc, and a number of trials are underway.Current trials need to proceed in the landscape of previous studies and should incorporate lessons learned from earlier success or failure.Prospective randomized clinical trials of patients with SSc-ILD have been challenging, but even studies that are unequivocally negative, such as BUILD-2 (1), have provided valuable prospective data on the natural history of SSc-ILD and the measurement variability of common clinical trial endpoints.The most recent SSc-ILD trial is SLS2 that compared oral cyclophosphamide to mycophenolate mofetil in 126 patients with SSc-ILD (2).This study, together with SLS1 that compared oral cyclophosphamide to placebo (3), provides a large and wellcharacterized cohort that can be used to assess change in lung function parameters over 12 and 24 months.These two studies have similar study populations, endpoints, and follow up durations, providing a valuable opportunity to integrate patient-level data and perform a combined analysis to address challenging questions.
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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 | 0.169 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".