Sleep Disturbances in Patients with Persistent Delusions: Prevalence, Clinical Associations, and Therapeutic Strategies
Bibliographic record
Abstract
Sleep disturbances accompany almost all mental illnesses, either because sound sleep and mental well-being share similar requisites, or because mental problems lead to sleep problems, or vice versa. The aim of this narrative review was to examine sleep in patients with delusions, particularly in those diagnosed with delusional disorder. We did this in sequence, first for psychiatric illness in general, then for psychotic illnesses where delusions are prevalent symptoms, and then for delusional disorder. The review also looked at the effect on sleep parameters of individual symptoms commonly seen in delusional disorder (paranoia, cognitive distortions, suicidal thoughts) and searched the evidence base for indications of antipsychotic drug effects on sleep. It subsequently evaluated the influence of sleep therapies on psychotic symptoms, particularly delusions. The review's findings are clinically important. Delusional symptoms and sleep quality influence one another reciprocally. Effective treatment of sleep problems is of potential benefit to patients with persistent delusions, but may be difficult to implement in the absence of an established therapeutic relationship and an appropriate pharmacologic regimen. As one symptom can aggravate another, comorbidities in patients with serious mental illness all need to be treated, a task that requires close liaison among medical specialties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".