<p>Neuro-Ophthalmological Manifestations of Obstructive Sleep Apnea: Current Perspectives</p>
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a disease of obstructed airways during sleep that significantly impacts the quality of life and increases the risk of various systemic diseases. OSA has been studied as a risk factor for a number of neuro-ophthalmic conditions and a strong relationship has been established with non-arteritic anterior ischemic optic neuropathy (NAION). The incidence of glaucoma and stroke have also been significantly associated with OSA and are conditions that may also be seen by neuro-ophthalmologists. Patients with NAION have a significantly higher incidence of OSA and OSA diagnosis significantly increases the risk for NAION development. Non-compliance with continuous positive airway pressure (CPAP) in OSA patients has also been found to be a risk factor for fellow-eye involvement and there is increasing evidence to suggest that every patient with NAION should be formally evaluated with polysomnography. The relationship between OSA and idiopathic intracranial hypertension (IIH) has also been studied, but the relationship between these two conditions is less clear. There is insufficient evidence to recommend routine eye examinations in OSA patients for papilledema and conducting a sleep study for a newly diagnosed IIH patient should be left to the discretion of the clinician based on other symptoms and risk factors of OSA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".