Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnoea (OSA) is often overlooked in the context of schizophrenia because its hallmark, daytime sleepiness, is so easily attributable to antipsychotic drugs. This is a special problem for women. AIMS: To underscore the importance of diagnosing and treating OSA in women with schizophrenia. METHODS: A review of the recent literature (search terms: Obstructive Sleep Apnoea; Schizophrenia; Women (or Gender); Obesity; Antipsychotics; Continuous Positive Airway Pressure (CPAP)) as it applies to a composite case vignette taken from the files of a specialty clinic that treats women with psychosis. RESULTS: The rate of OSA in women who are both obese and postmenopausal is very similar to that of men. Family history, smoking, and the use of tobacco, alcohol and of antipsychotic medication increase the risk. Despite reluctance, patients with schizophrenia generally agree to undergo sleep studies. Compliance with CPAP is difficult, but can be aided by the physician and is, on the whole, relatively high in women. CPAP improves sleep parameters and may also improve cardiometabolic and cognitive indices, although this still needs to be more fully researched. CONCLUSION: Schizophrenia and untreated OSA are both associated with high mortality rates in women as well as men.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".