0626 A METHOD FOR IDENTIFICATION OF INSPIRATORY FLOW LIMITATION USING RESPIRATORY AIRFLOW
Bibliographic record
Abstract
Inspiratory flow limitation (IFL) is a common component of sleep-disordered breathing, but current methods of identification require catheterization of the pharynx. The objective of this study was to develop and validate an accurate, non-invasive method of identifying IFL. Participants with high upper airway resistance underwent a full-night polysomnogram wearing temporary dental trays connected to a remotely controlled mandibular positioner (MATRx). A pressure transducer connected to a saline-filled nasopharyngeal catheter measured supra-glottic pressure, and respiratory airflow was calculated from naris pressure. These two signals were processed by an auto-labeler (AL) which identified IFL breaths that served as a “gold standard”. A neural network (NN) was trained on six patients using airflow as input and the gold standard as ideal output. The trained NN was then validated on three new patients using airflow as the sole input with no ideal output. In training, the AL identified 17652 breaths (61%) as IFL and 11220 (39%) as non-IFL; in validation, the AL identified 8123 breaths (59%) as IFL and 5650 (41%) as non-IFL. A 5-fold cross validation on the training data yielded an area under ROC curve of 0.86. The area under the ROC using the trained NN on all validation breaths was 0.89, and this increased to 0.90 when equivocal breaths, comprising 10% of the total, were excluded. A non-invasive method of identifying IFL was developed by training a NN using a “gold standard” and respiratory airflow. When applied to a new population of patients, the trained NN, using airflow alone, showed reasonable accuracy in identifying IFL breaths. This was improved by excluding equivocal breaths, yielding an area under ROC curve of 0.90 on 90% of all breaths. The authors acknowledge Alberta Innovates - Technology Futures, NRC-IRAP, and Zephyr Sleep Technologies for supporting this research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".