Short Form-McGill Pain Questionnaire-2 (SF-MPQ-2): A Cross-Cultural Adaptation and Validation Study of the Persian Version in Patients with Knee Osteoarthritis.
Bibliographic record
Abstract
BACKGROUND: We aimed to develop and validate the Persian version of Short Form McGill Pain Questionnaire-2 (SF-MPQ-2) in patients with knee osteoarthritis. METHODS: Translation and back translation was performed using Beaton's guideline. After a consensus has achieved on the Persian version of SF-MPQ-2, it was administered to 30 patients with knee osteoarthritis in a pilot study. Then, we enrolled 100 patients with knee osteoarthritis to fill the final SF-MPQ-2 as well as SF-36 and WOMAC questionnaires. Forty-three patients returned 3 days after the initial visit to fill the Persian SF-MPQ-2 for the second time. Construct validity was tested by Pearson's correlation coefficient between subscales of SF-MPQ-2 and subscales of SF-36 and WOMAC. Internal consistency for total and subscales was calculated by Cronbach's alpha and reliability between test retest was performed using Intraclass correlation coefficient (ICC). RESULTS: ICC for subscales of SF-MPQ-2 ranged from 0.73 to 0.90. The ICC for total SF-MPQ-2 was 0.90. Cronbach's alpha for subscales was 0.65-0.74 at the first visit and 0.58-0.81 at the second visit. Cronbach's alpha for the total questionnaire was 0.88 and 0.91 at the first and second visit, respectively. Pearson's correlation coefficient was highly significant when comparing subscales specifically with WOMAC (r=-0.47 to -0.61; P<0.001). Interscale correlation between subscales of SF-MPQ-2 was significant as well (r: 0.43-0.88, P<0.001). CONCLUSIONS: Persian SF-MPQ-2 showed excellent reliability and good to excellent internal consistency throughout the questionnaire. It is a valid and reliable instrument for measuring the pain intensity and applicable in osteoarthritic pain assessment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".