Understanding the relationship between obstructive sleep apnea and comorbid neurocognitive and affective disorders in older adults
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) syndrome is characterized by disruptions in oxygen flow resulting in nocturnal choking, loud snoring, and poor sleep. OSA impacts older adults at a disproportionate rate, and it is highly comorbid with neurocognitive and affective disorders. Untreated OSA is a significant risk factor for accelerated cognitive decline and affective disturbances in the aging population. This narrative review was the first to our knowledge to examine the complexity of an interplay between OSA and these comorbidities and provide recommendations for researchers and healthcare practitioners. We highlighted the patterns of under- and misdiagnosis of OSA and neurocognitive disorders in primary care settings due to gaps in construct validity of existing measures, inconsistent terminology and operationalization of neurocognitive and affective disorders, and lack of resources. Pervasive under- or misdiagnosis of OSA and its comorbidities in combination with overwhelmingly poor CPAP treatment compliance negatively impact the geriatric population. Some of the examples provided in this review reflect the Canadian context of the authors practice, although the research gaps and clinical recommendations are applicable more broadly.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".