Effects of Obstructive Sleep Apnea-Hypopnea Syndrome and Cognitive Function in Ischemic Stroke Based on Linear Regression Equation
Post-publication record
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Bibliographic record
Abstract
The objective of this research is to study the effect of obstructive sleep apnea-hypopnea syndrome on cognitive function of stroke. Based on linear regression equation and Montreal Cognitive Assessment Scale, the degree of cognitive impairment in OSAHS patients was evaluated and the influencing factors of OSAHS-induced cognitive impairment and the correlation between the degree of OSAHS and cognitive impairment were explored. The results are as follows: about 68% of OSAHS patients have cognitive dysfunction, and the incidence of cognitive dysfunction is positively correlated with OSAHS; cognitive impairment of OSAHS patients was associated with age, obesity, years of schooling, and intermittent nocturnal hypoxia or hypoventilation; the severity of cognitive dysfunction of OSAHS patients was positively correlated with age and obesity but negatively correlated with education level; Logistic regression analysis results showed that there were three factors that were finally entered into the regression equation, namely, LSaO2, BMI, and AHI, and the Logistic regression equation obtained was as follows: Logist P = − 0.109 X 1 + 0.785 X 2 + 1.228 X 3 . This study helps clinical workers to detect and intervene the impaired cognitive ability of patients with OSAHS early, so as to reduce the incidence and mortality of related complications and improve the quality of life of patients.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".