Validation of a modified McGill Pain Questionnaire for orthodontic patients
Bibliographic record
Abstract
OBJECTIVE: To assess better and more efficiently the aspects of pain experienced by adolescent orthodontic patients, the McGill Pain Questionnaire-Short Form (MPQ-SF) was modified (MMPQ-SF) and validated. MATERIALS AND METHODS: Internal and external expert panels developed a MMPQ-SF with 15 descriptors and 4-point Likert severity scales (MMPQ-SF15). Seventy-five subjects undergoing orthodontic treatment gave informed consent. Sixty-one subjects completed the MMPQ-SF15, a visual analogue scale (VAS), and the present pain index (PPI) 24 hours after an orthodontic visit. Respondents were grouped by gender and treatment stage: initial (15 female, 7 male), middle (10 female, 7 male), and end (16 female, 6 male). MMPQ-SF, VAS, and PPI scores were compared by Spearman correlation analysis. Underlying constructs were explored by factor analyses. Construct validity of the MMPQ-SF was tested by analysis of variance. RESULTS: MMPQ-SF15 and VAS (r = 0.78, r(2)s = 0.61, P < .0001), MMPQ-SF15 and PPI (r = 0.84, r(2)s = 0.71, P < .0001), and VAS and PPI (r = 0.70, r(2)s = 0.48, P < .0001) were correlated positively and significantly. A two-factor solution (localized and generalized/emotional pain; Kaiser-Meyer-Olkin = 0.88) showed that 11 descriptors (MMPQ-SF11) accounted for 64% of response variability. Generalized/emotional pain values were significantly higher during the initial stage of treatment compared to the middle (P = .011) and end stages (P = .014). CONCLUSIONS: MMPQ-SF, particularly MMPQ-SF11, demonstrated utility in assessment of localized and generalized/emotional aspects of pain in adolescent orthodontic patients and correlated well with VAS and PPI. Future application of the MMPQ-SF11 to measure orthodontic pain more specifically could lead to more appropriate management of this important facet of therapy.
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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 | 0.011 | 0.022 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".