Bibliographic record
Abstract
Main Take-Aways Evidence-based guidelines recommend against the use of quetiapine for the treatment of insomnia. The findings of this review suggest that antipsychotics such as quetiapine should not be used in the treatment of insomnia. What Is the Issue? Quetiapine is an atypical or second-generation antipsychotic drug that is primarily used for treating schizophrenia and bipolar disorder. Quetiapine extended-release is also indicated by Health Canada for the symptomatic relief of major depressive disorder when currently available approved antidepressant drugs have failed. Decision-makers are interested in evidence on the clinical effectiveness, safety, and place in therapy of quetiapine relative to other medications for the treatment of insomnia. What Did We Do? We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2020. What Did We Find? Evidence-based guidelines recommend against the use of quetiapine for the treatment of insomnia due to potential concerns about safety and lack of evidence of clinical benefit. What Does It Mean? Current evidence-based guidelines advise against prescribing quetiapine for the treatment of insomnia due to safety concerns and a lack of demonstrated clinical benefit. The findings of this review suggest that antipsychotics such as quetiapine should not be used in the treatment of insomnia.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".