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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".