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 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".