A preliminary fuzzy model for screening obstructive sleep apnea
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a common sleep disorder, especially in middle-aged and obese patients. OSA only occurs during sleep, and is a condition that may go unnoticed for years. OSA is associated with increased risk for hypertension, congestive heart failure and coronary artery disease. Furthermore, patients with OSA have higher risk during and after anesthesia. While undiagnosed OSA is a hazard for the patient, treatment is relatively simple and effective. Therefore, analysis of OSA risk factors and early detection of OSA are critical. This paper describes a fuzzy model for the screening of OSA. The screening questionnaire we are focused on is the STOP-Bang questionnaire. It is made up of eight yes or no questions to assess the risk of OSA. The problem with this model is that lines of low and high risk are too clearly drawn. There are certain predictors and combinations of predictors that have a greater weight in assessing the patient's risk. Thus, we introduce a fuzzy-logic based model for the screening of OSA. This model more accurately assesses the risk of OSA by assigning weights to certain predictors that play a greater role in OSA.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".