Systemic Sclerosis Classification: A Rose by Any Other Name Would Smell As Sweet?
Bibliographic record
Abstract
The 1980 preliminary criteria for scleroderma (systemic sclerosis; SSc) have worked well for decades1. The monumental thinking of the authors is to be applauded. However, over time things have changed. There are more patients with SSc who are on the mild end of the spectrum and who are in the limited cutaneous SSc (lcSSc) subset; this may be due to the evolution of the disease, more recognition because of available commercial autoantibodies, earlier diagnosis, or all these reasons. The 2013 criteria also incorporate the 3 main features of SSc (although not all patients have all features): vasculopathy, fibrosis, and autoantibodies. Raynaud phenomenon (RP) is included as a feature even though it does not distinguish from other patients with RP, but because SSc without RP is so rare, RP adds statistical value to the criteria2,3. In other cohorts, similar operational characteristics (sensitivity and specificity) have been reported4,5,6. The 2013 criteria can classify more patients that experts would label as having SSc. A patient with sclerodactyly, RP, positive anticentromere antibody, and proven pulmonary arterial hypertension would be classified as having SSc by the 2013 criteria but not by the 1980 criteria. The same applies for someone with sclerodactyly, RP, anticentromere antibodies, dysphagia, dilated nailfold capillaries, and calcinosis. However, a patient with only sclerodactyly, gastroesophageal reflux disease, dilated lower esophagus, dysphagia, RNA polymerase III, and scleroderma renal crisis would not meet either set of SSc criteria. But as soon as the scleroderma progressed beyond the fingers, the patient would satisfy both classifications. The 2013 SSc classification criteria may need some explanation and clarification. The footnote in Table 12,3 would … Address correspondence to Dr. Pope, St. Joseph’s Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada. E-mail: janet.pope{at}sjhc.london.on.ca
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".