Impact of wait times for treatment on clinical outcomes in patients with obstructive sleep apnoea: protocol for a randomised controlled trial
Bibliographic record
Abstract
Background: Obstructive sleep apnoea (OSA) is a common chronic condition that is associated with significant morbidity and economic cost. Prolonged wait times are increasingly being recognised as a barrier to diagnosis and treatment of many chronic diseases; however, no study to date has prospectively evaluated the impact of wait times on health outcomes in OSA. Objective: standard pathway. Methods: A pragmatic randomised controlled trial design will be used with a target sample size of 200 adults. Participants with clinically significant uncomplicated OSA will be recruited through referrals to a large tertiary care sleep centre (Calgary, AB, Canada) and randomised to either early management (within 1 month) or usual care (∼6 months) with a 1:1 allocation using a concealed computer-generated randomisation sequence. The primary outcome will be adherence to positive airway pressure (PAP) therapy at 3 months after treatment initiation. Secondary outcomes will include change in sleepiness, quality of life, patient satisfaction, and patient engagement with therapy from baseline to 3 months after PAP initiation, measured using validated questionnaires and qualitative methods. Anticipated results: This study will determine whether expedited care for OSA leads to differences in PAP adherence and/or patient-reported outcomes. More broadly, the findings of this study may improve the understanding of how wait time reductions impact health outcomes for other chronic diseases.
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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 | 0.062 | 0.065 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.095 | 0.016 |
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".