Assessing Cognitive Impairments in Obstructive Sleep Apnea Patients Using Montreal Cognitive Assessment (MoCA) Scores
Bibliographic record
Abstract
Obstructive Sleep Apnea (OSA) is a chronic condition associated with cognitive impairment and various comorbidities. This prospective study evaluated cognitive deficits in OSA patients and identified clinical factors affecting cognitive function. Seventy-two participants were assessed using polysomnography (PSG) and the Montreal Cognitive Assessment (MoCA). Findings revealed significantly lower MoCA scores in severe OSA patients compared to those with mild or moderate OSA. Severe OSA patients had a median MoCA score of 23.5 (20.0-25.0), indicating more significant cognitive impairment, while those with normal OSA severity had the highest median score of 28.5 (27.8-29.2). Mild and moderate OSA patients had median scores of 26.5 (21.0-28.0) and 25.0 (23.80-26.0), respectively (p < 0.008). Logistic regression showed that ex-smoking status negatively impacted MoCA scores more in the unadjusted model (p = 0.003) than in the adjusted one (p = 0.018). Forced Vital Capacity (FVC) positively correlated with MoCA scores, stronger in the unadjusted model (p < 0.001 vs. p < 0.03). Higher Oxygen Desaturation Index (ODI) correlated with higher MoCA scores while increasing Apnea-Hypopnea Index (AHI) severity correlated with lower MoCA scores in both models. A significant negative correlation was found between age and MoCA score (r = -0.473, p < 0.001), and between MoCA score and AHI (r = -0.350, p < 0.003). This study highlights the need for sensitive cognitive screening tools like MoCA in evaluating OSA patients, linking cognitive impairment closely with OSA severity and other clinical factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".