Exploration of a pain assessment tool on burning mouth syndrome
Bibliographic record
Abstract
BACKGROUND: Burning mouth syndrome (BMS) is a chronic orofacial pain disorder. The etiology and pathophysiology of BMS remain unclear; multiple factors may interact in complex ways. There is a need for simpler and more cost-effective BMS evaluation criteria. This study aimed to evaluate the reliability and validity of the Short Form McGill Pain Questionnaire version 2 (SF-MPQ-2) in patients with BMS and develop a subscale based on factor analysis of the results to classify patients per their symptoms. METHODS: Several factors such as patient characteristics (age, sex, smoking habit, and medical history), the SF-MPQ-2 (original: eleven-point rating scale and modified: four-point rating scale), and the numerical rating scale (NRS) of BMS were examined and analyzed. RESULTS: In total, 38 patients were enrolled. Cronbach's alpha was 0.93 (0.88-0.96) and 0.83 (0.74-0.90) for the SF-MPQ-2 (original) and SF-MPQ-2 (Modified), respectively. Only the correlation between the NRS and the SF-MPQ-2 (Modified) reached statistical significance. These results showed that the SF-MPQ-2 (Modified) were more reliable than the SF-MPQ-2 (Original). Factor analysis led to classification into three new factors. CONCLUSIONS: SF-MPQ-2 was useful for BMS. In current clinical practice, the modified questionnaire may yield similar or better results, and a more precise treatment strategy can be pursued by classifying responses according to the proposed subscales and examining treatment effects.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".