The Impact of Smoking Status on Obstructive Sleep Apnea: Insights from Anthropometric and Physiological Covariates*
Bibliographic record
Abstract
Smoking status has been implicated as a risk factor for various respiratory disorders, but its relationship with obstructive sleep apnea (OSA) remains controversial. This study explored the association between smoking status (current, former, and non-smokers) and apnea-hypopnea index (AHI) across smoking groups while considering anthropometric parameters such as sex, age, BMI, Mallampati score, and neck circumference. A Gamma generalized linear model (GLM) was used to determine the effect of smoking status in the presence of physiological covariates including sex, age, BMI, and Mallampati score. Findings revealed that current-smokers had significantly different AHI values compared to former-smokers and non-smokers, particularly within male and high neck circumference subgroups. Moreover, the adjusted GLM model showed that the effect of smoking status on AHI was attenuated when these covariates were considered. The results of this study are encouraging to be further investigated in larger and more balanced datasets while considering the smoking cessation duration.Clinical Relevance- This study explores the potential impacts of smoking status on the severity of obstructive sleep apnea. Understanding the underlying association helps clinicians consider smoking history during OSA diagnosis and management alongside other key anthropometric features. The finding could guide clinicians in developing personalized treatment strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".