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Predisposição da apneia e seus impactos na sonolência diurna e qualidade de vida na obesidade

2025· article· W4417473929 on OpenAlexaboutno aff
Caio Zimermann Oliveira, Jéssica Lie Utiamada, Vanessa Valgas dos Santos

Bibliographic record

VenueMedicina (Ribeirão Preto) · 2025
Typearticle
Language
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
Fundersnot available
KeywordsEpworth Sleepiness ScaleExcessive daytime sleepinessAnthropometryObesityBody mass indexObstructive sleep apneaQuality of life (healthcare)

Abstract

fetched live from OpenAlex

This research evaluated the risk of obstructive sleep apnea through apnea questionnaires and scales in obese patients and examined their impact on quality of life. For this, 51 obese patients of both sexes had their anthropometric and biochemical parameters measured. The presence of respiratory disorders was assessed using the NoSAS score (Neck, Obesity, Snoring, Age, Sex). The risk of apnea was verified using the STOP-BANG questionnaire (Snore, Tiredness, Observed, Blood Pressure, Body mass index, Age, Neck, Gender). We measured daytime sleepiness using the Epworth scale. The impact of apnea on quality of life was assessed using the Quebec Sleep Questionnaire. STOP-BANG results showed that 88.24% of women and 100% of men were at high risk for apnea. In addition, 70.45% of women and 42.85% of men had hypersomnolence on the Epworth with a score above 10. The groups classified as <10, 10-16, and >16 on the Epworth were evaluated through the sleep questionnaire, and the results showed significant differences in all domains between the <10 and >16 groups (p < 0.01). We concluded that obese patients have a high risk for apnea, with consequences on daytime sleepiness that impact the quality of life of these individuals.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.005
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.339
Teacher spread0.319 · 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; both teacher heads agree on what is shown here.

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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