The Contribution of Insomnia and Obstructive Sleep Apnea on the Transition from Acute to Chronic Painful Temporomandibular Disorders and their Persistence: A Prospective 3-Month Cohort Study
Bibliographic record
Abstract
Insomnia and excessive daytime sleepiness, a surrogate marker of obstructive sleep apnea, are common sleep-related conditions among painful temporomandibular disorders (TMD) subjects. Obstructive sleep apnea was found to increase the risk of chronic painful TMD. This prospective cohort study aims to determine the contribution of insomnia and excessive daytime sleepiness (ESS/OSA) on acute to chronic painful TMD transition as well as its persistence when chronic pain is defined by: (i) duration (> 3 months), and (ii) dysfunction (Graded Chronic Pain Scale [GCPS II-IV]). From 456 subjects recruited between 2015 to 2021, through four locations in Canada, 378 completed the follow-up. A diagnosis was obtained using the Research Diagnostic Criteria or the Diagnostic Criteria for Temporomandibular Disorders. Insomnia was assessed with the Insomnia Severity Scale (ISS), and excessive daytime sleepiness was measured using the Epworth Sleepiness Scale (ESS/OSA), both at baseline. Subjects completed the GCPS form at baseline and 3-month follow-up. Borderline associations were found between ESS/OSA and the transition or persistence of chronic painful TMD when chronic pain was defined by pain duration (RR adjusted_duration = 1.11, P = 0.07) and dysfunction (RRadjusted_dysfunction =1.40, P = 0.051). Furthermore, ESS/OSA was specifically associated with persistent painful TMD when chronic pain was defined by pain duration (RR = 1.13, 95%CI: 1.00-1.26, P = 0.04). Insomnia was not related to the study outcomes (RRadjusted_duration = 0.94, P = 0.27, RRadjusted_dysfunction =1.00, P = 0.99). Results indicate that ESS/OSA contrary to insomnia predicted the persistence of chronic painful TMD at a 3-month follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".