The Localized Scleroderma Skin Severity Index and Physician Global Assessment of Disease Activity: A Work in Progress Toward Development of Localized Scleroderma Outcome Measures
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate a Localized Scleroderma (LS) Skin Severity Index (LoSSI) and global assessments' clinimetric property and effect on quality of life (QOL). METHODS: A 3-phase study was conducted. The first phase involved 15 patients with LS and 14 examiners who assessed LoSSI [surface area (SA), erythema (ER), skin thickness (ST), and new lesion/extension (N/E)] twice for inter/intrarater reliability. Patient global assessment of disease severity (PtGA-S) and Children's Dermatology Life Quality Index (CDLQI) were collected for intrarater reliability evaluation. The second phase was aimed to develop clinical determinants for physician global assessment of disease activity (PhysGA-A) and to assess its content validity. The third phase involved 2 examiners assessing LoSSI and PhysGA-A on 27 patients. Effect of training on improving reliability/validity and sensitivity to change of the LoSSI and PhysGA-A was determined. RESULTS: Interrater reliability was excellent for ER [intraclass correlation coefficient (ICC) 0.71], ST (ICC 0.70), LoSSI (ICC 0.80), and PhysGA-A (ICC 0.90) but poor for SA (ICC 0.35); thus, LoSSI was modified to mLoSSI. Examiners' experience did not affect the scores, but training/practice improved reliability. Intrarater reliability was excellent for ER, ST, and LoSSI (Spearman's rho = 0.71-0.89) and moderate for SA. PtGA-S and CDLQI showed good intrarater agreement (ICC 0.63 and 0.80). mLoSSI correlated moderately with PhysGA-A and PtGA-S. Both mLoSSI and PhysGA-A were sensitive to change following therapy. CONCLUSION: mLoSSI and PhysGA-A are reliable and valid tools for assessing LS disease severity and show high sensitivity to detect change over time. These tools are feasible for use in routine clinical practice. They should be considered for inclusion in a core set of LS outcome measures for clinical trials.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".