Obstructive sleep apnea and chronic pain as risk factors of cognitive impairment in elderly population: A study from Indonesia
Bibliographic record
Abstract
Obstructive sleep apnea (OSA), one of the most prevalent sleep-related breathing disorders in the elderly, seems to be underdiagnosed. Meanwhile, the resulting complication on cognitive function could impact on patient's quality of life. Association between OSA and cognitive function in the elderly varies highly, depending on study type, setting, and possibly by demographic differences. Therefore, this study sought to determine the risk of OSA among elderly and to assess the association of OSA risk and other plausible factors with cognitive function. In this cross-sectional study, patients aged 60 years and above who visited the outpatient clinic at two main hospitals in Surabaya of Indonesia were examined. A total of 178 participants were interviewed to evaluate the OSA risk using STOP-Bang questionnaire, the cognitive dysfunction using Montreal Cognitive Assessment Indonesian version (MoCA-Ina), depressive symptoms using Geriatric Depression Scale-15 (GDS-15), and sleep disorder using Insomnia Screening Questionnaire (ISQ). The Mann-Whitney and Chi-square tests were used to assess factors associated with cognitive impairment. In addition, logistic regression analyses were performed to evaluate the role of high risk of OSA on cognitive impairment. A total of 120 patients were considered having high risk of OSA (STOP- Bang score ≥3), and 129 had mild cognitive impairment (MCI) (MoCA-Ina <26). Among the elderly who had high risk of OSA, 94 were diagnosed with MCI (78.3%). Multivariate logistic regression analysis showed that high risk of OSA (OR: 2.99; 95%CI: 1.39, 6.46, p=0.005), chronic pain (OR: 5.53; 95%CI: 1.19, 25.64, p=0.029), and low education level (OR: 4.57; 95%CI: 1.79, 11.63) were associated with MCI. In conclusion, our data suggests a high prevalence of MCI among high risk OSA elderly. Screening and comprehensive management might be beneficial to improve or to preserve cognitive function in elderly group.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".