Utilization of Healthcare Resources in Obstructive Sleep Apnea Syndrome: a 5-Year Follow-Up Study in Men Using CPAP
Bibliographic record
Abstract
STUDY OBJECTIVES: Patients with untreated obstructive sleep apnea syndrome (OSAS) have higher healthcare utilization than matched controls. However, the long-term impact of continuous positive airway pressure (CPAP) use on healthcare utilization is unknown. DESIGN: Retrospective observational cohort study. SUBJECTS: There were 342 eligible men with OSAS and matched controls on whom there were utilization data for 5 years prior to initial OSAS diagnosis and for the 5 years on CPAP treatment of the cases. INTERVENTIONS: Patients were treated with CPAP. RESULTS: Patients with OSAS were typical cases (mean +/- SD): age, 48.2 +/- 0.6 years; body mass index, 35.6 +/- 0.4 kg/m2; Epworth Sleepiness Scale score, 14.2 +/- 0.3; apnea-hypopnea index, 47.1 +/- 1.8 events per hour. The number of physician visits were higher by 3.46 +/- 0.2 (95% confidence interval [CI]: 2.57 to 4.36) in cases in the year before diagnosis, compared with the fifth year before diagnosis, then decreased over the next 5 years by 1.03 +/- 0.49 (95% CI: -1.99 to -0.07)(P<.0001). Physician fees, in Canadian dollars, were higher by dollars 148.65 +/- dollars 27.27 (95% CI: 95.12 to 202.10) in cases in the year before diagnosis, compared with the fifth year before diagnosis, and then decreased over the next 5 years by dollars 13.92 +/- dollars 27.94(95%CI: -68.68 to 40.83)(P=.0009). Preexisting ischemic heart disease at the time of OSAS diagnosis predicted about a 5-fold increase in healthcare utilization between the second and fifth year of treatment. CONCLUSIONS: Treatment of OSAS reversed the trend of increasing healthcare utilization seen prior to diagnosis. Preexisting ischemic heart disease results in a negative impact on healthcare utilization. CPAP results in a long-term health benefit, as measured by the use of healthcare services.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".