The Contribution of Sleep Quality and Psychological Factors to the Experience of Within-Day Pain Fluctuations Among Individuals With Temporomandibular Disorders
Bibliographic record
Abstract
We assessed the impact of day-to-day sleep quality and psychological variables (catastrophizing, negative affect, and positive affect) to within-day pain fluctuations in 42 females with painful temporomandibular disorders (TMD) using electronic diaries. More specifically, we examined the contribution of these variables to the likelihood of experiencing pain exacerbations defined as 1) an increase of 20 points (or more) in pain intensity on a 0 to 100 visual analog scale from morning to evening, and/or 2) a transition from mild-to-moderate pain over the course of the day; and pain decreases defined as 3) a decrease of 20 points (or more) in pain intensity (visual analog scale) from morning to evening, and/or 4) a reduction from moderate-to-mild pain over the day. The results indicated significantly main effects of sleep on both pain exacerbation outcomes (both P's < .05), indicating that nights with better sleep quality were less likely to be followed by clinically meaningful pain exacerbations on the next day. The results also indicated that days characterized by higher levels of catastrophizing were associated with a greater likelihood of pain exacerbations on the same day (both P's < .05). Daily catastrophizing was the only variable significantly associated with within-day pain decrease indices (both P's < .05). None of the other variables were associated with these outcomes (all P's > .05). These results underscore the importance of addressing patients' sleep quality and psychological states in the management of painful TMD. PERSPECTIVE: These findings highlight the significance of sleep quality and pain catastrophizing in the experience of within-day pain fluctuations among individuals with TMD. Addressing these components through tailored interventions may help to alleviate the impact of pain fluctuations and enhance the overall well-being of TMD patients.
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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) | 0.001 | 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".