Stroke profile in patients with high risk of obstructive sleep apnea: a transversal study
Bibliographic record
Abstract
Introduction: Obstructive sleep apnea (OSA) is a disease of upper airways, diagnosed with polisomnography, that is well associated with stroke throught diverse mechanisms. Despite that, it still lacks studies about stroke profile in patients with high risk of OSA. Objectives: This study aims to describe the diferences between stroke in patients with high and low risk of OSA. Methods: We performed a transversal study, part of a cohort of stroke patients. Patients were evaluated of risk of OSA with STOP-Bang and SOS scores; also, data of wake-up stroke, stroke type and etiology, transient ischemic attack (TIA), thrombolysis, circulation evolved, ASPECTS (Alberta stroke program early computed tomography score), PC-ASPECTS (posterior circulation - acute stroke prognosis early computed tomography score), hemorragic transformation and its type (ECASS), ABCD2, National Institute of Health Stroke Scale (NIHSS) and death during internment were colectted. Results: We obtained 190 patients. 62.1% were classified as high risk with STOP-Bang and (mean NIHSS 6.59 ± 5.13) and 8.5% died: 5.9% had a TIA (mean ABDC2 5.14 ± 1.21), 22% a wake-up stroke and 81.4% had a ischemic stroke (24 patients were cardioembolic, 34 atherotrombotic and 26 indeterminate), of which 12.7% were thrombolysed and 15.3% had hemorragic transformation. In ´patients with low risk, 2.8% died and we had mean NIHSS 7.64 ± 5.55, 18.3% had wake up stroke and 8.5% a TIA (mean ABCD2 4.17 ± 1.60), 74.6% were ischemic strokes (24% cardioembolic, 23 indeterminated); 12.7% patients were thrombolysed and 21% evolved with hemorragic transformation. Conclusion: We verified a lower NIHSS mean and a higher rate of wake-up stroke, hemorragic transformation and TIA in patients with low risk of OSA. Althought this would suggest better outcomes in population with high risk, they had 5% more deaths: so, despite the aparent better initial state (that we can hypothesize about a type of “lucid interval” in this group), they still evolve worst.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".