Subsets in systemic sclerosis: one size does not fit all
Bibliographic record
Abstract
Purpose Systemic sclerosis (SSc) is a heterogeneous disease that is often divided into subsets to stratify patients and predict prognosis. We hypothesized that individual methods of subsetting would not prognosticate equally well for different outcomes or in patients at different stages of disease. Methods We subsetted subjects with SSc using three approaches: limited versus diffuse cutaneous SSc (lcSSc, dcSSc); grouped by SSc-specific antibodies; and, grouped using unsupervised clustering. We studied patients with <2 years or between 2-4 years of disease duration, separately. Outcomes were time to death and time to development of (a) SF-36 Physical Component Score <40, (b) forced vital capacity <70% predicted, (c) echocardiographic pulmonary hypertension, and (d) interstitial lung disease. We used Cox proportional hazards models to determine the ability of the subsets to predict the outcomes of interest, and Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare the performance of the models. Results In this international, multicentered cohort of over 500 SSc subjects with less than four years of disease duration, none of the three methods of subsetting studied was able to predict all of the outcomes of interest. Different subsetting methods predicted different outcomes within and between each disease duration group. In general, subsetting by skin performed somewhat better than the two other methods, but this was not consistent and there was considerable variability in the models. Conclusions Subsetting SSc to consistently predict morbidity and mortality in subjects at different stages of disease remains an important challenge.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".