Can Large Simple Trials Help Us Understand When and How to Use Generic Drugs for Uncommon Diseases?
Bibliographic record
Abstract
Treatment of interstitial lung disease associated with connective tissue disease (CTD-ILD) is an area of high unmet medical need. A 1-year cyclophosphamide (CYC) treatment regimen improved scleroderma (SSc)-associated ILD (SSc-ILD) in a randomized placebo-controlled trial1, but failed to sustain improvement after 2 years2. A second multicenter trial of 6 months of CYC for SSc-ILD did not demonstrate efficacy3. Even given a possible modest benefit, CYC is undesirable for chronic or repetitive use due to its toxicity, making the ascertainment of other treatments for CTD-ILD a high priority. In this issue of The Journal , Fischer, et al 4 report apparent broad utility of mycophenolate mofetil (MMF) in the treatment of CTD-ILD. MMF has been gaining acceptance for treatment of CTD-ILD based on earlier small studies, including those of the authors5 and others6,7,8,9,10,11 and a recent metaanalysis12. This body of work suggests that MMF can confer stability or modest improvement in pulmonary function tests (PFT). The Scleroderma Lung Study-II (SLS-II) [a US National Institutes of Health (NIH)-sponsored randomized, blinded comparison of 2-year treatment with MMF versus 1-year treatment with CYC, plus 1-year followup for SSc-ILD] completed enrollment of about 140 subjects (http://clinicaltrials.gov/show/NCT00883129). It will be 2 more years, however, before analysis of SLS-II is completed. The Fischer, et al study, although neither randomized nor blinded, has several important strengths. First, it comprises the largest published groups of MMF-treated patients with SSc-ILD as well as other CTD-ILD. Second, there appears to be broad benefit of MMF, irrespective of the specific CTD diagnosis, or of ILD histologic subtype. The study benefits from relatively uniform dosing of MMF at 2 grams per day … Address correspondence to Dr. Molitor; E-mail: jmolitor{at}umn.edu
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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 | 0.188 | 0.407 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".