Depressive symptoms account for differences between self‐reported versus polysomnographic assessment of sleep quality in women with myofascial <scp>TMD</scp>
Bibliographic record
Abstract
Summary Patients with temporomandibular disorder ( TMD ) report poor sleep quality on the Pittsburgh Sleep Quality Index ( PSQI ). However, polysomnographic ( PSG ) studies show meagre evidence of sleep disturbance on standard physiological measures. The present aim was to analyse self‐reported sleep quality in TMD as a function of myofascial pain, PSG parameters and depressive symptomatology. PSQI scores from 124 women with myofascial TMD and 46 matched controls were hierarchically regressed onto TMD presence, ratings of pain intensity and pain‐related disability, in‐laboratory PSG variables and depressive symptoms (Symptoms Checklist‐90). Relative to controls, TMD cases had higher PSQI scores, representing poorer subjective sleep and more depressive symptoms (both P < 0·001). Higher PSQI scores were strongly predicted by more depressive symptoms ( P < 0·001, R 2 = 26%). Of 19 PSG variables, two had modest contributions to higher PSQI scores: longer rapid eye movement latency in TMD cases ( P = 0·01, R 2 = 3%) and more awakenings in all participants ( P = 0·03, R 2 = 2%). After accounting for these factors, TMD presence and pain ratings were not significantly related to PSQI scores. These results show that reported poor sleep quality in TMD is better explained by depressive symptoms than by PSG ‐assessed sleep disturbances or myofascial pain. As TMD cases lacked typical PSG features of clinical depression, the results suggest a negative cognitive bias in TMD and caution against interpreting self‐report sleep measures as accurate indicators of PSG sleep disturbance. Future investigations should take account of depressive symptomatology when interpreting reports of poor sleep.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".