A Fuzzy Model for the Evaluation of Efficacy of Continuous Positive Airway Pressure (CPAP) Treatment
Bibliographic record
Abstract
Obstructive sleep apnea hypopnea (OSAH) is a serious, chronic respiratory disorder afflicting approximately 2-4% of the general population. The standard treatment of OSA is the continuous positive airway pressure (CPAP). CPAP treatment is highly effective; however, it is not curative and its efficacy depends highly on patient's life-long compliance. Modeling of the effectiveness of this therapy involves several interrelated and, often, subjective factors. This paper describes a model, called CPAP-VAL, for the evaluation of CPAP treatment based on three main factors: improvements in symptoms (nocturnal blood oxygen desaturation, excessive daytime sleepiness, hypertension, and depressive moods), CPAP treatment compliance (average hours of use, percentage of days used, and percentage of CPAP use per total hours of sleep), and patient's characteristics (age, gender, and OSAH severity). The proposed model uses the fuzzy logic approach to combine subjective and objective measurements and to represent complex interrelationships between various factors. The CPAP-VAL model was designed as an evaluation component for a telehealth system, CPAP-T*MONITOR, which will support the treatment process for OSAH patients living in the rural areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".