Patient Preference Assessment Reveals Disease Aspects Not Covered by Recommended Outcomes in Polymyositis and Dermatomyositis
Bibliographic record
Abstract
Objectives. Polymyositis (PM) and dermatomyositis (DM) are characterized by impaired muscle function with a majority of patients developing sustained disability. The aim of this study was to evaluate the patient's individual priorities (patient preference) of disabilities most important to improve in PM/DM using the MacMaster Toronto Arthritis Patient Preference Disability Questionnaire (MACTAR), to correlate the MACTAR to myositis outcomes and to evaluate its test-retest reliability. Methods. Twenty-eight patients with PM/DM performed recommended outcomes as well as the MACTAR, which was performed twice with one week apart. Results. Sexual activity, walking, biking, social activities, and sleep constituted the predominating disabilities. Seventy-two and 33% of the identified disabilities were not covered by items of the Health Assessment Questionnaire and the Myositis Activities Profile. Correlations between the MACTAR and health-related quality of life measures were r(s) = -0.67-0.73, correlations with measures of activities of daily living and participation in society were r(s) = 0.51-0.60 with lower correlations for other outcomes. Intraclass correlation (ICC) and weighted Kappa (K(w)) coefficients were 0.83 and 0.68, respectively, for test-retest reliability of the MACTAR. Conclusions. The MACTAR interview had promising measurement properties and identified patient preference disabilities in PM/DM that were not covered by recommended outcomes.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".