Evaluation of the clinimetric properties of the Early Inflammatory Arthritis--self-administered comorbidity questionnaire
Bibliographic record
Abstract
OBJECTIVES: To adapt the self-administered comorbidity questionnaire (SCQ) into the Early Inflammatory Arthritis-SCQ (EIA-SCQ) and assess its clinimetric properties in EIA. METHODS: The EIA-SCQ and indices of disease activity, function, pain, health-related quality of life (HRQoL) and health resource utilization were administered to 320 patients with EIA. Twenty patients completed the EIA-SCQ a second time 1 week later. Construct validity was evaluated by testing the hypotheses that a valid comorbidity index would correlate well with age, weakly with HRQoL and recent resource utilization and poorly with indices of disease activity, function and pain. RESULTS: The intra-class correlation coefficient between repeat scores was 0.93 (95% CI 0.83-0.97). Kappa values for individual items ranged from 0.64 to 1.0. EIA-SCQ scores correlated moderately with age (Tau B = 0.29, P < 0.001) and weakly with function (HAQ-DI Tau B = 0.09, P = 0.03), pain (McGill Pain Questionnaire Tau B = 0.09, P = 0.05), some measures of HRQoL [the SF-36 mental component score (MCS) Tau B = - 0.08, P < 0.05; World Health Organization Disease Assessment Schedule II score Tau B = 0.09, P = 0.03] and a measure of resource utilization (number of tests in the last 4 months Tau B = 0.10, P = 0.04). The EIA-SCQ did not correlate with other measures of disease activity, another HRQoL measure [SF-36 physical component score (PCS)] or other measures of resource utilization. CONCLUSIONS: The EIA-SCQ is reliable and valid for use in EIA. It has the potential to become a useful measure of comorbidity in outcome studies of EIA when the resources for a full medical chart review are unavailable.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".