Development and validation of Arabic version of the Short-Form Mcgill Pain Questionnaire
Bibliographic record
Abstract
Introduction: The Short-Form McGill Pain Questionnaire (SF-MPQ) is a widely used tool for qualitative and quantitative pain assessment. Our aim was to translate, culturally adapt, and validate the SF-MPQ in Arabic.Methods: A systematic translation process was used to translate the original English SF-MPQ into Arabic. After the pilot study, we validated our version in patients with chronic pain at two tertiary care centers. We tested the reliability of our version using internal consistency and test-retest reliability. We examined the validity by assessing construct validity, concurrent validity (by investigating the associations between SF-MPQ, Brief Pain Inventory [BPI], and Self-completed Leeds Assessment of Neuropathic Symptoms and Signs [S-LANSS]), and face validity. The questionnaire was administered twice to examine responsiveness.Results: A total of 142 participants (68 men and 74 women) were included in this study. Cronbach's α was 0.85 (95% confidence interval: 0.81– 0.89), and interclass correlation coefficients were 0.71 (0.62–0.79) for the whole scale. SF-MPQ was moderately associated with patients' present pain (r = 0.55, P< 0.001) and the numerical rating scale (r = 0.42, P< 0.001). The total pain score was moderately correlated with pain severity and interference assessed with the BPI (rs = 0.39 to 0.49, all Ps < 0.001). SF-MPQ total pain score was weakly associated with neuropathic pain assessed with S-LANSS (r = 0.26, P< 0.01). Most patients found the SF-MPQ questions to be clear and easy to understand and thought the questionnaire items covered all their problem areas regarding their pain.Conclusion: Our translated version of SF-MPQ was reliable and valid for use among Arabic-speaking patients. The SF-MPQ is a good qualitative and quantitative assessment tool for pain but is only weakly associated with neuropathic pain.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".