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Record W2048850508 · doi:10.3109/09638237.2013.869572

Diagnosis and treatment of sleep apnoea in women with schizophrenia

2014· review· en· W2048850508 on OpenAlexaff
Mary V. Seeman

Bibliographic record

VenueJournal of Mental Health · 2014
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychiatrySleep (system call)Schizophrenia (object-oriented programming)Context (archaeology)AntipsychoticExcessive daytime sleepinessMedicinePsychosisPsychologySleep disorderInsomnia

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.387
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2014
Admission routes1
Has abstractyes

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