Pressure-pain threshold variation in temporomandibular disorder myalgia over the course of the menstrual cycle.
Bibliographic record
Abstract
AIMS: To evaluate the influence of the menstrual cycle on pressure-pain thresholds (PPTs) in patients with masticatory myalgia. METHODS: Fluctuations in pain sensitivity during 2 consecutive menstrual cycles were assessed in 15 normally menstruating patients with a myogenous temporomandibular disorder (TMD). Muscle pain was measured by the use of pressure algometry and a visual analog scale (VAS). The McGill Pain Questionnaire was used to assess the sensory, affective, and evaluative dimensions of the pain. RESULTS: Since 5 patients dropped out of the study due to pregnancy, unexpected menstrual cycle irregularities, or personal problems, statistical analysis was performed on 10 patients. Time had a significant influence on the pain condition. The PPTs of all muscle sites increased significantly and progressively over time by 16% to 42% in the follicular and luteal phases. PPTs remained low in the perimenstrual phase. The VAS pain rating did not correspond well with the PPTs, and the statistical analysis showed that the VAS ratings could not be used as predictors for the PPT measurements or detect the differences between cycle phases. The sensory, affective, and evaluative dimensions of the pain were significantly lower at the end of the trial. CONCLUSION: These data suggest a significant influence of the menstrual cycle on pain report and a nonspecific improvement of the chronic myogenous TMD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".