Prediction of severity of obstructive sleep apnea by awake impulse oscillometry
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: Obstructive sleep apnea (OSA) is a common disease, which poses a significant health threat. Initial diagnostics with polygraphy or polysomnography are time consuming and expensive. Therefore, there is an unmet medical need for simplification, especially to exclude healthy patients from elaborate and unnecessary diagnostics. Impulse oscillometry (IOS) is a simple, cheap and noninvasive tool to asses upper airway resistance, which is increased in patients with OSA. The objective was to examine the relationship between IOS parameters and polysomnography in order to evaluate the applicability of IOS as a supplementing tool in OSA diagnostics. PATIENTS/METHODS: We performed a prospective, cross-sectional, observational study across 107 participants. Pulmonary function tests with IOS, bodyplethysmography and overnight polysomnography were performed. We computed direct and partial correlations between IOS- and PSG-results. ROC analysis was performed to evaluate the most impactful predictive IOS parameter for diagnosing OSA. RESULTS: In ROC analysis the predicted probability of resistance at 5Hz (R5%) combined with age showed the highest AUC of 0.919, while R5 at 0.4325kPa/(l/s) provided the optimal cut-off. Correlations between IOS parameters and OSA severity as well as the duration and severity of oxygen desaturation were observed. However, they could not be reproduced as partial correlations after eliminating the BMI as confounding variable. CONCLUSION: Our results cannot indicate the usefulness of IOS in OSA diagnostics. The lack of BMI-independent partial correlations between IOS- and PSG-results suggest a correlation without causality fallacy between IOS- and PSG-results. Therefore, the initial impression of good test quality for IOS might be invalid.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".