The Current Status of Autotitrating Continuous Positive Airway Pressure Systems in the Management of Obstructive Sleep Apnea
Bibliographic record
Abstract
BACKGROUND: Autotitrating continuous positive airway pressure (APAP) devices have the potential to address some of the disadvantages of titration and treatment with conventional continuous positive airway pressure (CPAP). Information on the performance of APAP in clinical use is still comparatively limited. OBJECTIVE: To assess the status of APAP devices in the management of obstructive sleep apnea (OSA) by reviewing evidence of their efficacy, effectiveness and costs. METHODS: A systematic search of electronic databases and a review of selected comparative studies on the use of APAP in the diagnosis, titration and treatment of OSA was undertaken. Cost analysis using data applicable to the management of OSA in Edmonton, Alberta was performed. RESULTS: Thirty-three studies met the selection criteria: three on the use of APAP in diagnosing OSA; six on APAP for titration; 14 that considered short-term treatment outcomes; and 10 that addressed longer-term treatment of OSA. In most studies, patients suffering from cardiac, pulmonary and other medical conditions were excluded. Available data suggested some potential for the use of APAP in the diagnosis of OSA, but further validation is needed. In titration, estimated treatment pressures tended to be lower with APAP than with the manual titration of CPAP. Although lower treatment pressures were achieved with APAP, there was no significant difference in clinical outcome measures between APAP and CPAP. Estimates of costs suggested that APAP may provide savings in some scenarios. CONCLUSIONS: APAP shows promise in the management of OSA; however, given the exclusion of some categories of patients from trials of this technology, caution is still required in its use.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".