An Algorithmic Approach to the management of Insomnia in Patients with Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Insomnia is an important problem in patients with schizophrenia and is an emerging area of interest for researchers. We propose a treatment algorithm that synthesizes the various psychological and pharmacological interventions for insomnia in this population. METHODS: Our selective literature review incorporates English language articles from 4 medicine databases through May 2016. Selected articles discuss risk factors and treatments for insomnia, as well as comorbid sleep disorders that coexist in this population. RESULTS: Various lifestyle factors and comorbid sleep disorders may predispose patients with schizophrenia to insomnia. Cognitive-behavioral therapy for insomnia shows promising results in treating insomnia in patients with schizophrenia spectrum disorders. Additionally, studies of eszopiclone and melatonin have yielded significant results in short-term trials that evaluated both subjective and objective insomnia symptoms. CONCLUSIONS: We have summarized the relevant literature regarding the treatment of insomnia in this patient population and propose an algorithm comprising 6 sequential steps, beginning with the assessment of sleep complaints and medication adherence. This is followed by a targeted treatment of any co-occurring sleep disorders, and ends with psychoeducation, cognitive-behavioral therapy, and pharmacotherapy. This algorithm provides a detailed guideline to improve the assessment and therapeutic intervention for managing insomnia among patients with schizophrenia.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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".