The McMaster Toronto Arthritis patient preference questionnaire (MACTAR): a methodological study of reliability and minimal detectable change after a 6 week-period of acupuncture treatment in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: The McMaster Toronto Arthritis patient preference questionnaire (MACTAR) is a semi-structured interview consisting of a baseline and a follow-up interview. The MACTAR baseline is reliable and valid, however the reliability of the MACTAR follow-up is scarcely described. The aim of this study was to describe aspects of reliability and ability to detect changes of the Swedish MACTAR follow-up following acupuncture treatment in individuals with rheumatoid arthritis. RESULTS: The study was of Single Subject Experimental Design, with a 2-week non-interventional A-phase and a 6-week intervention B-phase. Eight individuals with RA, age 30-68 years, were included. MACTAR baseline was performed once followed by five assessments with MACTAR follow-up during the A-phase and another ten assessments during the B-phase. Reliability statistics were calculated for measurements 1-3 during the A-phase and the ability to detect effects of acupuncture treatment was tested by celeration lines in the B-phase. The MACTAR follow-up was highly reliable (ICC = 0.7-0.9, SEM = 2.3-4.3, and SDD = 6.2-11.7). Visual and statistical analyses indicated that the MACTAR follow-up could detect effects on individual- and group levels after acupuncture treatment, indicating that the MACTAR follow-up seems to be reliable and is able to detect effects of acupuncture treatment in 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 | 0.037 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".