Patient preference disability questionnaire in systemic sclerosis: A cross‐sectional survey
Bibliographic record
Abstract
OBJECTIVE: To assess patient priorities concerning disability in systemic sclerosis (SSc). METHODS: A total of 150 SSc patients (22 men) fulfilling the American College of Rheumatology and/or LeRoy and Medsger criteria for SSc were evaluated by the McMaster Toronto Arthritis Patient Preference Disability Questionnaire (MACTAR), Karnofsky performance status (KPS), Cochin Hand Function Scale, Health Assessment Questionnaire (HAQ), Hospital Anxiety and Depression Scale, Mouth Handicap in SSc (MHISS) scale, and global perception regarding their health status. Correlations between scores were analyzed using Spearman's coefficient. Logistic regression analysis was used to determine factors associated with patients' global perception of their health. RESULTS: Of the patients investigated, 81 (54%) had limited cutaneous SSc, 65 (43.3%) diffuse SSc, and 4 (2.7%) limited SSc. The 3 disability domains most often cited were walking (82 patients [54.6%]), housekeeping (67 patients [44.6%]), and sport activities (59 patients [39.3%]). The MACTAR score correlated moderately with KPS (r = 0.58) but only weakly with the HAQ score (r = 0.38). In multivariate analysis, 2 factors were associated with patients' negative global perception of their health status: KPS (odds ratio [OR] 1.07, 95% confidence interval [95% CI] 1.00-1.15) and MHISS score (OR 0.93, 95% CI 0.88-0.99). CONCLUSION: For assessing SSc patient priorities concerning disability, the MACTAR has acceptable construct validity. Its weak correlation with the HAQ suggests that it adds useful information on disability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".