Retinal Sensitivity Is Reduced in Patients With Obstructive Sleep Apnea
Bibliographic record
Abstract
PURPOSE: To evaluate the outcomes of standard automated perimetry (SAP) in patients with obstructive sleep apnea (OSA). METHODS: Eighty OSA patients and 111 age-matched controls were consecutively and prospectively enrolled. One eye per subject was randomly selected. All participants underwent at least one reliable SAP (24-2 SITA Standard algorithm). The peripapillary retinal nerve fiber layer thickness (RNFL) was measured with spectral-domain optical coherence tomography (OCT). Patients with OSA were classified into three groups according to the apnea/hypopnea index: mild, moderate, or severe OSA. Parameters of SAP and OCT were compared between healthy controls and OSA patients. Correlation of apnea/hypopnea index with OCT and SAP measurements were calculated. RESULTS: Mean age, best-corrected visual acuity, and central corneal thickness were similar between groups. Intraocular pressure, however, was lower in the OSA group. Mean deviation of SAP was -0.23 ± 0.8 dB in the control group and -1.74 ± 2.8 dB in the OSA group (P < 0.001). Thickness of RNFL measured with OCT did not differ significantly between groups. Patients with OSA showed reduced sensitivity at most points tested by white-on-white perimetry compared with healthy individuals. The threshold values were more depressed in the peripheral visual field. The apnea/hypopnea index was related to the SAP indices: Pearson correlations were -0.432 with mean deviation, 0.467 with pattern standard deviation, and -0.416 with the visual field index (P < 0.001). CONCLUSIONS: Patients with OSA exhibited reduced retinal sensitivity measured with SAP compared with healthy controls.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".