Bibliographic record
Abstract
We designed the current study to describe the spectrum of disease expression in systemic sclerosis (SSc) in a large cohort and to develop diagnostic criteria for SSc. We assessed patients in the Canadian Scleroderma Research Group Registry by standardized history, physical examination, and laboratory testing. We performed regression tree analysis to determine the sensitivity of various clinical and serologic features for diagnosing SSc. Over 1000 (n = 1048) patients were included: mean age 55 (± 12) years, 87% female, 90% white, mean disease duration 11 (± 10) years, and 38% with diffuse skin involvement. Common clinical features were Raynaud phenomenon (98%), sclerodactyly (92%), clinically visible mat-like telangiectasias (78%), skin involvement above the fingers (58%), lung fibrosis (35%), pulmonary hypertension (15%), and gastrointestinal tract involvement (mean number of self-reported symptoms, 4 (± 3) out of a possible 14). Almost 90% of patients had at least 1 SSc-related autoantibody, including 34% with anti-centromere and 16% with anti-topoisomerase I. The sensitivity of Raynaud and proximal finger skin thickening for the diagnosis of SSc was only 57%. Addition of clinically visible mat-like telangiectasias and SSc-related antibodies improved the sensitivity to 97%. We conclude that important diagnostic clues in patients with SSc include Raynaud phenomenon, skin involvement, clinically visible mat-like telangiectasias, and SSc-related autoantibodies. Abbreviations: ACR = American College of Rheumatology, CES-D = Center for Epidemiologic Studies Depression Scale, CI = confidence interval, CSRG = Canadian Scleroderma Research Group, HAQ = Health Assessment Questionnaire-Disability Index, PM/Scl = polymyositis/scleroderma, RNA Pol III = RNA polymerase III, SF-36 = MOS 36-item Short-Form Health Survey, S-HAQ = Scleroderma-Health Assessment Questionnaire, SSc = systemic sclerosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".