Immunosuppressive Drugs in Early Systemic Sclerosis and Prevention of Damage Accrual
Bibliographic record
Abstract
OBJECTIVE: Organ damage in patients with systemic sclerosis (SSc) in individual organs such as the lungs may be prevented by receiving immunosuppressive drugs (ISs). A new measure of global organ damage, the Scleroderma Clinical Trials Consortium Damage Index (SCTC-DI), has allowed us to investigate whether receiving ISs may reduce global organ damage accrual in patients with early SSc. METHODS: This was a retrospective study of patients with two or less years of disease duration in Canadian and Australian cohorts with SSc. Patients with either limited cutaneous SSc (lcSSc) or diffuse cutaneous SSc (dcSSc) were observed separately and divided into groups who were either ever or never exposed to ISs. The SCTC-DI was the outcome, and inverse probability of treatment weighting (IPTW) was used to balance the study groups and to fit a marginal structural generalized estimating equation model. RESULTS: In the cohort with lcSSc, there were 210 patients, of whom 34% were exposed to ISs at some time. Exposure to ISs was associated with lower damage scores. In the cohort with dcSSc, there were 192 patients, of whom 76% were exposed to ISs at some time. Exposure to ISs was not associated with damage scores. CONCLUSION: In this retrospective observational cohort study, using IPTW to adjust for confounders, we found a protective effect of receiving ISs on damage accrual in patients with lcSSc. We were unable to determine such an effect in patients with dcSSc, but unknown confounders may have been present, and prospective studies of patients with dcSSc receiving ISs should include the SCTC-DI to determine the possible effect of ISs on damage accrual.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".