The Dermatomyositis Disease Symptom Questionnaire (DM-DSQ): A Measure to Assess the Patient Experience of Dermatomyositis Symptoms
Bibliographic record
Abstract
OBJECTIVE: Dermatomyositis (DM) symptoms negatively affect the quality of life of individuals living with the disease. Disease-specific, patient-reported outcome (PRO) instruments are needed to assess symptoms important to individuals with DM. This study aimed to conceptualize patient DM experience and disease activity definition to refine the development of the Dermatomyositis Disease Symptom Questionnaire (DM-DSQ), a novel PRO instrument capturing patient-reported symptoms. METHODS: An observational, qualitative study was conducted with 30 individuals with DM (aged ≥ 18 yrs) in the US. A 1-hour semistructured interview, including concept elicitation and cognitive debriefing, was conducted with each participant. Inductive coding was used to identify concepts; a saturation analysis was conducted to confirm sample size. Concepts from transcripts were used to refine the preliminary conceptual model and DM-DSQ items. RESULTS: Concept elicitation analysis findings included disease symptoms (eg, muscle weakness) and functional impacts (eg, walking). The analysis achieved conceptual saturation; the first 5 interviews uncovered most of the concepts. During cognitive debriefing of the DM-DSQ, participants found the items relevant, comprehensive, and easily understood (except for "skin sensitivity in sunlight"). The revised DM-DSQ content appears preliminarily valid in the patient population surveyed, pending further additions and debriefing based on refinement of the preliminary conceptual disease model and items. CONCLUSION: The DM-DSQ is being used in a phase II clinical trial and could become a valuable tool for studies evaluating PROs in patients with DM. Preliminary results indicate its content validity; extensive psychometric analysis using clinical trial data will determine its ability to capture symptoms for patients with DM.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".