Obesity, Sex, Snoring and Severity of OSA in a First Nation Community in Saskatchewan, Canada
Bibliographic record
Abstract
Sleep disorders have been related to body weight, social conditions, and a number of comorbidities. These include high blood pressure and type 2 diabetes, both of which are prevalent in the First Nations communities. We explored relationships between obstructive sleep apnea (OSA) and risk factors including social, environmental, and individual circumstances. An interviewer-administered survey was conducted with adult participants in 2018−2019 in a First Nations community in Saskatchewan, Canada. The survey collected information on demographic variables, individual and contextual determinants of sleep health, and objective clinical measurements. The presence of OSA was defined as an apnea−hypopnea index (AHI) ≥5. Multiple ordinal logistic regression analysis was conducted to examine relationships between the severity of OSA and potential risk factors. In addition to the survey, 233 men and women participated in a Level 3 one-night home sleep test. Of those, 105 (45.1%) participants were reported to have obstructive sleep apnea (AHI ≥ 5). Mild and moderately severe OSA (AHI ≥ 5 to <30) was present in 39.9% and severe OSA (AHI ≥ 30) was identified in 5.2% of participants. Being male, being obese, and snoring loudly were significantly associated with severity of OSA. The severity of OSA in one First Nation appears relatively common and may be related to mainly individual factors such as loud snoring, obesity, and sex.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".