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Record W2145542763 · doi:10.2147/ndt.s90521

Obstructive sleep apnea: management considerations in psychiatric patients

2015· review· en· W2145542763 on OpenAlexaff
Monica Zolezzi, Taryn Heck

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2015
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of Alberta HospitalAlberta Health Services
Fundersnot available
KeywordsMedicineObstructive sleep apneaContinuous positive airway pressureComorbidityPsychiatryPsychiatric comorbidityMental illnessIntensive care medicineMental healthInternal medicine

Abstract

fetched live from OpenAlex

Psychiatric disorders and obstructive sleep apnea (OSA) are often comorbid. However, there is limited information on the impact of psychotropic medications on OSA symptoms, on how to manage psychiatric pharmacotherapy in patients presenting with OSA, or on the effectiveness and challenges of OSA treatments in patients with comorbid mental illness. As such, the objective of this article is to provide an overview of some epidemiological aspects of OSA and treatment considerations in the management of OSA in individuals with comorbid psychiatric disorders. Predefined keywords were used to search for relevant literature in electronic databases. Data show that OSA is particularly prevalent in patients with psychiatric disorders. The medical care that patients with these comorbidities require can be challenging, as some of the psychiatric medications used by these patients may exacerbate OSA symptoms. As such, continuous positive airway pressure continues to be the first-line treatment, even in patients with psychiatric comorbidity. However, more controlled studies are required, particularly to determine continuous positive airway pressure compliance in patients with mental illness, the impact of treating OSA on psychiatric symptoms, and the impact of the use of psychotropic medications on OSA symptoms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.036
GPT teacher head0.331
Teacher spread0.295 · 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.

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

Citations22
Published2015
Admission routes1
Has abstractyes

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