Bibliographic record
Abstract
To examine the relationship between smoking and sleep apnea. Cross-sectional case study University hospital sleep clinic We studied 3,509 patients. None Smoking history (past, present, neversmoker, pack-years of smoking) and nocturnal polysomnography was obtained in all patients. Data analysis was performed as follows: Patients were divided into groups based on apnea severity (to compare smoking), and on smoking severity (to compare apnea hypopnea index - AHI). The relationship between smoking and apnea was examined using univariate, multivariate, and logistic regression analysis. We found that patients with AHI greater than 50 were heavier smokers than nonapneic controls (14 ± 17 vs. 10 ± 17 pack-years, respectively). Conversely, heavy smokers had higher AHI than non-smokers (26.3 ± 28.3 vs. 19.7 ± 23.9, respectively). There was no significant relationship between AHI and smoking in multiple regression analysis. Although logistic regression revealed that heavy smokers (>30 pack-yrs) had almost twice the risk of having AHI greater than 50 compared to nonsmokers, the odds ratio fell below one after adjusting for age, BMI, and gender. In our patient population, smoking is not an independent risk factor for sleep apnea after adjusting for other confounding variables.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".