Clinical pathways and wait times for OSA care in Ontario, Canada: A population cohort study
Bibliographic record
Abstract
RATIONALE: Lengthy wait times for obstructive sleep apnea (OSA) in Canada remain a concern.OBJECTIVES: To describe the clinical pathway for OSA and determine wait times from assessment of OSA until continuous positive airway pressure (CPAP) treatment in Ontario.METHODS: We conducted a population-based cohort study from 2006 to 2013. We used billing information to identify clinic visits for sleep complaints and polysomnography (PSG). We calculated the time from primary care visits until diagnostic PSG (diagnostic time) and from PSG until CPAP initiation (treatment time) for each year. We used logistic regression to identify factors associated with diagnostic and treatment time >6 months.RESULTS: A total of 216 514 CPAP users were included. Most (52%) had a diagnostic PSG without a sleep assessment and 52% underwent a titration PSG. The median diagnostic wait for hospital and community-based facilities were 260 (IQR 87–869) days and 203 (36–838) days, respectively, with less than 50% of patients undergoing PSG within 6 months of PC assessment. The median treatment wait was 108 days (57–193) at hospital and 82 days (45–143) at community-based facilities; less than 70% started on CPAP within 6 months. Female sex, recent hospitalization and having diagnostic PSG at a hospital were associated with longer diagnostic and treatment times.CONCLUSIONS: The clinical pathway for assessment and treatment of OSA in Ontario is highly variable and current wait times in Ontario remain lengthy. Interventions to improve physician education, redistribute sleep medicine resources and adopting new technologies should be considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".