Identification of Obstructive Apnea Signatures Result in Phenotypes with Distinct Biometric Differences
Bibliographic record
Abstract
Positive airway pressure (PAP) is the gold-standard treatment for patients diagnosed with Obstructive sleep apnea (OSA). We believe personalized treatment may be possible by developing algorithms which have been tuned to specific patient phenotypes. Therefore, it was our objective to investigate if specific airflow-based patient phenotypes existed in patients diagnosed with OSA. Using an apnea-centered analysis, we developed frequency-based representations of the OSA events for 102 patients. We then developed distinct patient phenotypes using projection pursuit analysis while comparing biometric and treatment variables measured by PAP machine or SleepImage RingTM between phenotypes (ANOVA & post-hoc t-test). We identified four clusters associated with patient Obstructive Apnea Signatures (Fig.1). Phenotypes 1 and 3 were both characterized by lower obstructive-apnea-index compared to 2 and 4 (p<0.05). However, Phenotypes 1 and 2 were characterized by higher sleep quality index, stability, and lower fragmentation (scored by SleepImage RingTM) compared to 3 and 4 (p<0.05). Our results demonstrate the utility of Obstructive Apnea Signatures and the existence of specific signature-based phenotypes. We believe this marks the beginning of personalized PAP therapy, where treatment is tailored to the unique airflow signatures and the specific needs of different patient phenotypes. erj;66/suppl_69/PA3205/F1 F1 F1
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".