Sleep Disturbance in Adjustment Disorder and Depressive Episode
Bibliographic record
Abstract
Background: In this paper, we aimed to examine the patterns of sleep disturbance in adjustment disorder (AD) and depressive episode (DE), to examine the variables associated with sleep disturbance in AD and DE and associated impairment in functioning. Methods: This is a multi-centre case-control study of 370 patients: 185 patients with AD and 185 patients with a diagnosis of DE, recruited from the liaison psychiatry services of three Dublin hospitals. We examined the participants’ sleep pathology using the sleep disturbance items on the Schedule for Clinical Assessment in Neuropsychiatry, and the Inventory of Depressive Symptoms—Clinician-rated-30. Results: Patients with a diagnosis of AD were less likely to report disturbed sleep than those with a diagnosis of DE (p = 0.002). On multivariate analysis, sleep disturbance was significantly associated with greater severity of certain depressive symptoms: decreased appetite (p < 0.001) and psychomotor agitation (p = 0.009). Decreased appetite, younger age and single marital status were significantly associated with sleep disturbance in male patients, and decreased appetite and psychomotor agitation were significantly associated with sleep disturbance in female participants. Conclusions: This is the largest study to date which has examined sleep disturbance in adjustment disorder. Disturbance of sleep is a significant symptom in AD and may represent a potential target for treatment. With further research, patterns of sleep disturbance may be useful in differentiating AD from DE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".