The association of odds ratio product with respiratory and arousal measures in post-stroke patients
Bibliographic record
Abstract
Obstructive Sleep Apnea (OSA) affects up to 70% of post-stroke patients, complicating recovery and rehabilitation. This study aimed to evaluate the utility of the Odds Ratio Product (ORP), a continuous EEG-derived metric of sleep depth, in predicting conventional respiratory and arousal measures in stroke patients. We hypothesized that ORP metrics will predict conventional measures in patients with a history of stroke or Transient ischemic attack (TIA). A retrospective analysis was conducted on 113 stroke/ TIA individuals who underwent in-laboratory polysomnography (PSG). ORP metrics, including ORP nrem , ORP rem , ORP 9 , and Wake Intrusion Indices (WIIs), were analyzed using multivariate linear regression models. Models were stratified by OSA status. Standardized coefficients were used to assess associations with the apnea-hypopnea index (AHI), respiratory disturbance index (RDI), and arousal indices. ORP metrics demonstrated statistically significant associations with conventional respiratory and arousal measures, with varying predictive strength across models. Specifically, ORP nrem and WIIs exhibited strong predictive effects across all models. ORP 9 significantly predicted respiratory and arousal measures in the overall sample and the OSA subgroup, but its predictive value diminished in the non-OSA subgroup. ORP rem was statistically significantly associated with respiratory and arousal measures; however, its associations with arousal measures were weaker in participants with OSA compared to those without OSA. ORP metrics have the potential to refine OSA diagnoses and improve therapeutic strategies in post-stroke/TIA populations. Their integration into sleep assessments could facilitate early intervention and potentially optimize stroke recovery outcomes, addressing gaps in current evaluation methods. • ORP metrics predict conventional respiratory and arousal measures in stroke patients. • ORP metrics capture microstructural sleep changes, offering insights into post-stroke sleep. • Integrating ORP into stroke care may refine OSA diagnosis and management strategies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".