Assessment of macular and peripapillary choroidal thickness in non-arteritic anterior ischemic optic neuropathy: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Non-arteritic anterior ischemic optic neuropathy (NAION) is the most common optic neuropathy in adults aged ≥ 50 years. Transient non-perfusion or hypoperfusion of the optic nerve head circulation is believed to be the underlying cause of NAION. It has been suggested that peripapillary choroidal thickness (PCT) is altered after ischemic disorders of the optic nerve head, but the results have not always been consistent. To address this issue and provide evidence for the pathogenesis of NAION, we performed a meta-analysis to systematically evaluate macular choroidal thickness (MCT) and PCT in patients with NAION. METHODS: A comprehensive literature search of PubMed, Embase, Cochrane Library, and Web of Science databases was performed until August 31, 2022. The main inclusion criterion was a case-control study in which MCT and PCT were measured using optical coherence tomography in patients with NAION. Mean difference (MD) and 95% confidence interval were calculated for continuous estimates. The Review Manager (V5.40) was used for the analysis. RESULTS: Nine studies comprising 663 eyes (283 NAION eyes and 380 healthy control eyes) were included (Newcastle-Ottawa Scale score ≥ 5). MCT and PCT were higher in eyes with chronic NAION (MD = 19.16, P = .04; MD = 35.36, P < .00001) and NAION fellow eyes (MD = 30.35, P = .0006; MD = 29.86, P = .04) than in healthy controls. No difference was noted in the MCT between eyes with acute NAION and healthy controls (MD = 2.99, P = .87). CONCLUSION: Increased MCT and PCT may be important anatomical and physiological features of the eyes in patients with NAION.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.046 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".