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Record W4415782222 · doi:10.14814/phy2.70622

Overnight deep inspiration patterns in obstructive sleep apnea patients with and without asthma

2025· article· en· W4415782222 on OpenAlexaff
Shokoufeh Mousavi, Maryam Mohebbi, Parisa Adimi Naghan, Azadeh Yadollahi

Bibliographic record

VenuePhysiological Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAsthmaObstructive sleep apneaPolysomnographyBronchodilatorQuartileRespiratory system

Abstract

fetched live from OpenAlex

Asthma and obstructive sleep apnea (OSA) often co-occur, exacerbating respiratory difficulties and altering airway physiology during sleep. Respiratory obstructions in OSA usually terminate with deep inspiration, and in asthma, deep inspiration may function as a bronchodilator or induce bronchoconstriction. This study investigated deep inspiration patterns using polysomnography data from 202 OSA patients (Apnea-Hypopnea Index (AHI) > 5) in the Sleep Heart Health Study, including asthma patients and matched controls. Airflow signals were used to calculate the average amplitude of three post-event deep breaths (PEDB), as overshoots usually peak within these breaths. PEDB curves were compared within and between groups, each including 68 mild, 19 moderate, and 14 severe OSA cases (AHI > 30). In severe OSA, mean PEDB increased from the first to last sleep quartile in controls (p < 0.05) but showed no change in those with asthma (p > 0.05). PEDB values were higher in controls than in asthma patients with moderate and severe OSA. Reduced PEDB intensity in severe OSA with asthma suggests impaired bronchodilator effects of deep inspirations, possibly from chronic inflammation and fluid shifts. These findings enhance understanding of asthma-OSA interactions and the potential role of deep inspirations in mitigating overnight narrowing in lower airways.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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