Distance to Specialist Medical Care and Diagnosis of Obstructive Sleep Apnea in Rural Saskatchewan
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is the most common sleep-disordered breathing condition. Patients with OSA symptoms are often not diagnosed clinically, which is a concern, given the health and safety risks associated with unmanaged OSA. The availability of fewer practicing medical specialists combined with longer travel distances to access health care services results in barriers to diagnosis and treatment in rural communities. This study aimed to (1) determine whether the proportion of adults reporting OSA symptoms in the absence of a sleep apnea diagnosis in rural populations varied by travel distance to specialist medical care and (2) assess whether any distance-related patterns were attributable to differences in the frequency of diagnosis among adults who likely required this specialist medical care. We used a cross-sectional epidemiologic study design, augmented by analysis of follow-up survey data. Our study base included adults who completed a 2010 baseline questionnaire for the Saskatchewan Rural Health Study. Follow-up occurred until 2015. 6525 adults from 3731 households constituted our sample. Statistical models used log-binomial regression. Rural adults who reported the largest travel distances (≥250 km) to specialist medical care were 1.17 (95% CI: 1.07, 1.29) times more likely to report OSA symptoms in the absence of a sleep apnea diagnosis than those who reported the smallest (<100 km; referent) distances. However, the proportion of sleep apnea diagnoses was low and unaffected by reported travel distance among adults who likely required this specialist medical care. Our findings suggest factors other than travel distance may be contributing to the low sleep apnea diagnostic rate. This remains important as undiagnosed and untreated OSA has serious implications on the health of people and populations, but effective treatments are available. Health care access barriers to the diagnosis and treatment of OSA require evaluation to inform health care planning and delivery.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".