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Record W2091453177 · doi:10.1164/rccm.200404-458oc

Influence of Weight and Sleep Apnea Status on Immunologic and Structural Features of the Uvula

2004· article· en· W2091453177 on OpenAlexaff
Frédéric Sériès, Jamila Chakir, Dominick Boivin

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicineObstructive sleep apneaUvulopalatopharyngoplastyApneaElastinHypopneaSleep apneaAirwayBody mass indexPathologyInternal medicinePolysomnographyAnesthesia

Abstract

fetched live from OpenAlex

We investigated the influence of weight and obstructive sleep apnea status on inflammatory and histologic features of the uvula. Tissue samples resected during uvulopalatopharyngoplasty in 11 snorers without obstructive sleep apnea, 11 subjects with obstructive sleep apnea and of similar body mass index and age, and 8 additional obese subjects with obstructive sleep apnea were examined by immunohistochemistry and histologic staining techniques. The frequency and distribution of immune cells, the amount of collagen, and the integrity of the elastin fiber network were evaluated in proximal and distal uvular sections. T cell (CD4+, CD8+) and macrophage counts were higher in the more obese apneic subjects than in the other two groups. In all patients, T cell counts correlated with body mass index, but there was no relationship with the apnea-hypopnea index. A positive correlation was found between elastin fiber network disorganization score and apnea-hypopnea index. We conclude that (1) the amount of inflammatory markers is linked to obesity rather than to sleep-related breathing disorders, and (2) obstructive sleep apnea is associated with a structural alteration of the extracellular matrix of upper airway tissue.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.310
Teacher spread0.300 · 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 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

Citations73
Published2004
Admission routes1
Has abstractyes

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