Changes in Retinal Nerve Fiber Layer Thickness in Obstructive Sleep Apnea/Hypopnea Syndrome: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To evaluate and compare changes in retinal nerve fiber layer (RNFL) thickness in patients with obstructive sleep apnea/hypopnea syndrome (OSAHS). METHODS: The Cochrane Library, Medline, and Embase were screened using our key words. Results were carefully reviewed to ensure that the included studies met the inclusion/exclusion criteria, and the quality of the studies was assessed using the Newcastle-Ottawa Scale. All included studies categorized patients with OSAHS into 3 groups (mild, moderate, and severe), and measured average and 4-quadrant (temporal, superior, nasal, and inferior) RNFL thickness. All studies included a healthy control group. The weighted mean differences and 95% confidence intervals were calculated for the continuous outcomes. RESULTS: Ten case-control studies were included in the meta-analysis, consisting of a total of 811 OSAHS group and 868 healthy eyes. A meta-analysis of the data showed that the average RNFL thicknesses in the mild, moderate, and severe OSAHS groups were significantly decreased compared to healthy controls. Additionally, RNFL thickness was significantly reduced in all but the temporal quadrant in the moderate and severe OSAHS groups when compared to healthy controls. CONCLUSIONS: On the basis of these results, we suggest that peripapillary RNFL thickness as measured by optical coherence tomography could be a useful tool to monitor and assess the severity of OSAHS in patients. Further studies are required in order to differentiate these RNFL changes from glaucomatous changes. This has not been properly examined in any of the studies we were able to identify.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".