[Investigation on clinical pain features in temporomandibular disorders].
Bibliographic record
Abstract
OBJECTIVE: To investigate the pain features of temporomandibular disorders (TMD) by qualitatively and quantitatively analyzing patient multi-dimension sensations. METHODS: Two hundred and fifty patients with painful TMD from January to December 2005 were included, short-form mcgill pain questionnaire (SF-MPQ) and visual analog scale (VAS) were administered to assess patients' sensation, affection and intensity of pain. The data were analyzed by SAS 8.0 software. RESULTS: All patients were assigned to three groups including 139 joint pain, 47 muscle pain and 64 both joint and muscle pain group. In joint pain group, the total number of descriptors was 250, 1.80 in average; in muscle pain group, the total number was 99, 2.11 in average; in both joint and muscle pain group, the total number was 107, 1.64 in average. The sensitive descriptors most frequently chosen in all three groups were "aching", "heavy", and "tender". "Tiring-exhausting" and "sickening" were high frequency descriptors in affective items. There were higher affective scores in the muscle pain group than in others. Muscle pain group had a higher VAS score at rest than the other two (P < 0.05), but had a lower VAS score during function than the other two (P < 0.001). All three groups usually had no pain at rest, and complained a slight to moderate pain during function. CONCLUSIONS: TMD pain was generally slight to moderate; "aching", "heavy", and "tender" were the most frequently sensitive descriptors, while unpleasant feelings were described as "tiring-exhausting" and "sickening"; pain generally occurred or exacerbated during mandibular function; compared to joint pain, muscle pain had its own features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".