Relationship between Travel Time from Home to a Regional Sleep Apnea Clinic in British Columbia, Canada, and the Severity of Obstructive Sleep
Bibliographic record
Abstract
RATIONALE: In the majority of people with obstructive sleep apnea, the disorder remains undiagnosed. This may be partly a result of inadequate access to diagnostic sleep services. We thus hypothesized that even modest travel times to a sleep clinic may delay diagnosis and reduce detection of milder disease. OBJECTIVES: We sought to determine whether travel time between an individual's home and a sleep clinic is associated with sleep apnea severity at presentation. METHODS: We recruited patients referred for suspected sleep apnea to the University of British Columbia Hospital Sleep Clinic between May 2003 and July 2011. The patient's place of residence was geocoded at the postal code level. Travel times between the population-weighted dissemination areas for each patient and the sleep clinic were calculated using ArcGIS (ESRI, Redlands, CA) network analyst and the Origin-Destination matrix function. All patients underwent full polysomnography. MEASUREMENTS AND MAIN RESULTS: There were 1,275 patients; 69% were male, the mean age was 58 years. (SD = 11.9), and the mean apnea-hypopnea index was 22 per hour (SD = 21.6). In the univariate model, travel time was a significant predictor of obstructive sleep apnea severity (P = 0.02). After controlling for confounders including sex, age, obesity, and education, travel time remained a significant predictor of sleep apnea severity (P < 0.01). In the multivariate model, each increase in 10 minutes of travel time was associated with an increase in the apnea-hypopnea index of 1.4 events per hour. CONCLUSIONS: For reasons that remain to be determined, travel times are associated with the severity of obstructive sleep apnea at presentation to a sleep clinic. If the results can be verified at other centers, this may help guide the geographic distribution of sleep centers within a health care system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".