The McGill pain questionnaire in patients with myogenic facial pain and TMJ disorders
Bibliographic record
Abstract
The assessment of pathologies characterized by pain situated at the temporomandibular joint (TMJ) or cheek, consequent on disorders of the TMJ itself and/or of the craniofacial or masticatory muscles is still controversial. As verbal pain assessment techniques are of help in discriminating between different pain sensations, our purpose was to assess the discriminative capacity of the McGill Pain Questionnaire (MPQ) in patients with TMJ disorders or with myogenous facial pain (MP). The MPQ was administered to 57 TMJ and 28 MP patients. Weighted MPQ item scores, subscale Pain Rating Indexes (PRI), total PRI and the number of words chosen were calculated. Mean scores were tested for significant differences (Student's t) and the frequency with which each descriptor was chosen by the patients of both groups was also analysed. Furthermore, the data were processed through two systems based on a counter‐propagation neural network: the Self Organising Map (SOM) system, and a cluster‐like analysis. In the MP group 16 of 20 mean MPQ item scores and all mean PRI were significantly higher than those of the TMJ group. The SOM analysis was able to distribute the two groups in the two different halves of the map; only two of 28 MP cases (7%) and 12 of 57 TMJ cases (21%) were misplaced. The cluster‐like analysis based on the 20 MPQ item scores was able to correctly recognize 94·73% TMJ patients and 89·28% MP patients. In conclusion, the MPQ showed a consistent discriminative capacity between TMJ and MP patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".