The role of obstructive sleep apnea, neurofilaments and early CPAP intervention in post-stroke cognitive recovery
Bibliographic record
Abstract
Stroke is a leading cause of disability worldwide, with cognitive impairment following stroke influenced by a complex interplay of modifiable and non-modifiable risk factors. This study investigated the impact of obstructive sleep apnea (OSA) on cognitive outcomes after ischemic stroke (IS) and the predictive value of plasma neurofilament light chain (pNFL) levels. Seventy-three acute IS patients were analyzed, with 59 completing a three-month follow-up. Cognitive function (Montreal Cognitive Assessment, MoCA) was assessed. Patients underwent polygraphic screening for OSA in the acute phase, with treatment recommended when indicated, and pNFL levels measured at baseline and follow-up. Results showed that 93.2 % of IS patients had OSA. Forty (72.7 %) of OSA patients (moderate, severe OSA) were recommended continuous positive airway pressure (CPAP). CPAP-treated patients in the acute phase demonstrated cognitive improvement at three-month follow-up (CPAP-treated: MoCA 23 vs 25 points, CPAP indicated untreated, MoCA 22 vs 22 points, p = 0.05). However, long-term adherence to CPAP was poor - only 25 % remained on therapy at three months. While pNFL levels correlated with infarct volume and significantly decreased over time, no correlation was found between OSA severity and CPAP treatment. Regression analysis identified age, prior stroke history, and anxiety as key predictors of cognitive and functional post-stroke outcome. Early CPAP therapy could contribute to improved post-stroke cognitive performance. Decline in pNFL levels shows ongoing neuronal recovery; a direct relationship with OSA is inconclusive. Furthermore, advanced age, history of prior stroke, and anxiety symptoms emerged as significant contributors to poorer cognitive outcomes.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".