Reproducibility: Reliability and Agreement Parameters of the Revised Short McGill Pain Questionnaire Version-2 for use in Patients with Musculoskeletal Shoulder Pain
Bibliographic record
Abstract
Background: The Revised Short-Form McGill Pain Questionnaire Version-2 (SF-MPQ-2) is a multidimensional outcome measure designed to evaluate neuropathic and nonneuropathic pain. A recent systematic review found insufficient psychometric data with respect to musculoskeletal health conditions.Aims: The aim of this study was to describe the reproducibility (reliability and agreement) and internal consistency of the SF-MPQ-2 for use among patients with musculoskeletal shoulder pain.Methods: Eligible patients with shoulder pain from musculoskeletal (MSK) sources completed the SF-MPQ-2 at baseline (n = 195), and a subset did so again after 3 to 7 days (n = 48) if their response to the global rating of change scale remained unchanged. Cronbach’s alpha (α) and intraclass correlation coefficient (ICC[2,1]) were calculated. Standard error of measurement (SEM), group and individual minimal detectable change (MDC90), and Bland-Altman plots were used to assess agreement.Results: Cronbach’s α ranged from 0.83 to 0.95, suggesting very satisfactory internal consistency across the SF-MPQ-2 domains. Excellent ICC(2,1) scores were found in support of the total (0.95) and continuous (0.92) subscales; the remaining subscales displayed good ICC(2,1) scores (0.78–0.88). Bland-Altman analysis revealed no systematic bias between the test and retest scores (mean difference = 0.13 to 0.19). Though the best agreement coefficients were seen on the total scale (SEM = 0.5; MDC90 = 1.2, MDC90group = 0.3), they were acceptable for the SF-MPQ-2 subscales (SEM: range, 0.7–1; MDC90: range, 1.7–2.3; MDC90group: range, 0.4–0.5).Conclusions: The SF-MPQ-2 provides good to excellent test–retest reliability for multidimensional pain assessment among patients with musculoskeletal shoulder pain conditions.
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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 | 0.056 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.001 |
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".