TMJ disorders and myogenic facial pain: a discriminative analysis using the McGill Pain Questionnaire
Bibliographic record
Abstract
Our aim was to assess the discriminative capacity of the McGill Pain Questionnaire (MPQ) in patients with temporomandibular joint (TMJ) disorders or with myogenous facial pain (MP). The MPQ was administered to 57 TMJ and 28 MP patients who were also asked to assess the level of pain using the Visual Analog Scale (VAS). 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-test), and the frequency with which each descriptor was chosen by the patients in both groups was analyzed. Data were also processed through two systems based on a counter-propagation neural network: the Self-Organizing Map (SOM) system, and a cluster-like analysis. In the MP group, 16 out of 20 mean MPQ item scores and all mean PRI and VAS scores were significantly higher than those in the TMJ group. There was a marked difference in descriptor choice. In the TMJ group the following descriptors were chosen by 25% or more of the patients: tiring, troublesome, nagging, sore, tender, and aching. In the MP group the descriptors most frequently chosen were: 'exhausting' (57%), 'punishing' (50%), and pulling (47%). SOM analysis distributed the two groups in the two halves of the map: only two out of 28 MP cases (7%) and 12 out of 57 TMJ cases (21%) were misplaced. The cluster-like analysis based on the 20 MPQ item scores correctly recognized 94.73% of TMJ patients and 89.28% of MP patients. In conclusion, the MPQ consistently discriminated between TMJ and MP patients. Although the higher affective scores in the MP patients may be partly induced by higher levels of anxiety in these patients, the data convincingly show that the system's discriminative capacity relates to all MPQ subscores and to the majority of the MPQ items. Moreover, within the same item, the choice of verbal descriptors varies consistently between the two groups of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 teacher head, 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".