Disabilities of importance for patients to improve – using a patient preference tool in rheumatoid arthritis
Bibliographic record
Abstract
PURPOSE: To investigate, using the McMaster Toronto Arthritis patient preference disability questionnaire (MACTAR), disabilities most important to improve in Swedish patients with rheumatoid arthritis (RA) and to compare these with the pre-defined activities in the International Classification of Functioning (ICF) comprehensive core set for RA and the Stanford Health Assessment Questionnaire (HAQ). Also to categorize patient preference selected disabilities using the ICF, to correlate the MACTAR score to RA core set measures and to evaluate the MACTAR's test-retest reliability. METHODS: 45 patients with RA (median (md) age 59 years, diagnosis duration md 10 years) were included. Assessments included disease activity score (DAS28), timed-stands test (TST), shoulder function assessment (SFA), visual analogue scale for pain (VAS), HAQ, patients' global assessment of well-being (PGA) and the MACTAR. RESULTS: 58 disabilities were identified of which 17 were identified by at least 5 patients. 47% of them were represented in the Comprehensive ICF RA core set and 53% in the HAQ. 16/17 were categorized in the ICF activities and participation component. Correlations between the MACTAR and other measures were: DAS28 (rs -0.65), TST (rs -0.19), SFA (rs 0.38), VAS (rs -0.61), HAQ (rs -0.51) and PGA (rs -0.61). Weighted κ was 0.59. CONCLUSIONS: Half of the disabilities patients with RA identified by use of the MACTAR are not evaluated in the Comprehensive ICF core set for RA or the HAQ. MACTAR has moderate test-retest reliability. MACTAR can be considered to be used in addition to traditional RA outcomes and may potentially improve clinical assessment of patients with RA. IMPLICATIONS FOR REHABILITATION: RA has an impact on personal life areas. The MACTAR helps identify individual disease-related disabilities of importance to improve. The MACTAR provides an opportunity for individualized goal-setting in rehabilitation and can thus promote adherence in rehabilitation. MACTAR may potentially improve clinical assessment for patients with RA.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".