A multi-system model for interventions in continuous positive airway pressure treatment: A fuzzy logic approach
Bibliographic record
Abstract
Continuous positive airway pressure (CPAP) therapy is the most widely used treatment for patients with moderate to severe obstructive sleep apnea (OSA), a serious chronic respiratory disorder. Although CPAP is a highly effective therapy, it is not, however, curative, and it must be used regularly to ameliorate OSA symptoms. Thus, the success of the therapy depends on the patient's compliance and proper use of the CPAP machine. The compliance and continuing use of CPAP might be improved by well-timed and well-designed interventions. There are three major types of interventions: mechanical (applying different types of CPAP machines, altering the air pressure), educational/psychological (improving patient's understanding of treatment, communicating with the health providers), and lifestyle (modifying diet and sleep habits). Therefore, a unified model for various types of interventions is critical for the success of the treatment. In this paper, we propose a multi-system model based on a fuzzy logic approach to assess the extent of the mechanical, educational/psychological, and lifestyle interventions. The CPAP- therapy intervention model, called CPAP-INT, has been designed as a component of a larger system for the comprehensive support of OSA treatment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".