Improving the sensitivity of the American College of Rheumatology classification criteria for systemic sclerosis.
Bibliographic record
Abstract
OBJECTIVE: A large proportion of patients with limited systemic sclerosis (SSc) do not meet the current American College of Rheumatology (ACR) classification criteria for SSc. We undertook this study to determine whether the addition of easily available clinical variables, namely nailfold capillary abnormalities identified using a dermatoscope and visible telangiectasias, could improve the sensitivity of the current ACR classification criteria for patients with limited SSc. METHODS: Patients in the Canadian Scleroderma Research Group Registry with skin involvement distal to the metacarpophalangeal joints were identified and divided into two groups according to whether they fulfilled the current ACR classification criteria for SSc or not. Sensitivity of the criteria was calculated. Regression tree analysis was performed to determine whether the addition of nailfold capillary abnormalities identified using a dermatoscope and visible telangiectasias could improve the sensitivity of the criteria. RESULTS: One hundred and one (101) patients were included, in majority women with a mean age of 59 (+/- 13). Of these, 68 (67%) met the ACR classification criteria. The sensitivity of the criteria increased from 67% to 99% with the addition of nailfold capillary abnormalities identified using a dermatoscope and visible telangiectasias. CONCLUSIONS: The SSc research community would benefit from having better classification criteria to identify patients with limited SSc. The current classification criteria for SSc may be significantly improved by the inclusion of easily identified clinical variables including nailfold capillary abnormalities using a dermatoscope.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".