Hypervigilance to pain and sleep quality are confounding variables in the infrared thermography examination of the temporomandibular joint and temporal and masseter muscles
Bibliographic record
Abstract
OBJECTIVES: Verify whether hypervigilance to pain (HP) and sleep quality (SQ) are confounding variables in the infrared thermography (IT) examination of the temporomandibular joint and temporal and masseter muscles. METHODS: A cross-sectional and analytical study was conducted, collecting HP and SQ data from 80 participants without temporomandibular disorders (TMD), performing their IT and another 40 participants with TMD. For the selection of participants with and without TMD, the TMD Pain Screener questionnaire and axis I of the Diagnostic Criteria for Temporomandibular Disorders were applied. SQ was verified using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. For the HP assessment the Pain Vigilance and Awareness Questionnaire (PVAQ) was applied. And the IT was performed through a FLIR infrared sensor camera, model T650 Infrared. RESULTS: No significant correlations were found between SQ and the temperatures of the areas of interest (P > .05), and regarding HP, a statistically significant positive correlation was found with the dimensionless (ρ = 0.289) and non-dimensionless (ρ = 0.223) asymmetries of temporal muscle temperatures. In the temperature comparisons between the participants without TMD and the participants with TMD, significant differences were found (P < .05), also when the group without TMD was controlled according to both HP and SQ (P < .05), with higher temperatures found in the TMD group. CONCLUSIONS: HP and SQ can be considered confounding variables in IT examination of the temporomandibular region.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".