Pain characteristics of temporomandibular disorder: a pilot study in patients with cervical spine dysfunction
Bibliographic record
Abstract
OBJECTIVE: The purpose of the present pilot study was to describe pain complaints of TMD patients and cervical spine dysfunction. METHODS: Fourteen women with myogenous TMD, cervical motion limitation and rotation of at least one of the three first cervical vertebrae evidenced by radiographic examination participated in this study. The multidimensional pain evaluation was accomplished by a Brazilian version of the McGill Pain Questionnaire. RESULTS: The results showed that the most painful body site mentioned was cervical spine, followed by scapular region and temporomandibular joint. More than half of the volunteers reported temporal pain pattern as rhythmic, periodic and, or still, intermittent. The majority of the patients classified the pain intensity assessed at the moment of the evaluation as mild to discomforting. Absolute agreement was not observed among volunteers regarding word dimensions used to describe their pain, although a great number of patients chose the descriptor related to tension as the better expression to describe their painful complaint. CONCLUSION: Pain characteristics of TMD patients with cervical spine dysfunction showed cervical spine as a common painful region reported and words related to affective and emotional dimensions of pain perception can be used by these patients to qualify their pain complain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".