Mapping Retinal Abnormalities in Psychosis: Meta-analytical Evidence for Focal Peripapillary and Macular Reductions
Bibliographic record
Abstract
BACKGROUND: Several studies have suggested that the retina structure is affected in schizophrenia spectrum disorders (SSD). We aimed to investigate the location and size of the potential differences between patients and healthy controls (HC) in several thickness and volume measures across the retina. STUDY DESIGN: We included cross-sectional studies comparing peripapillary retinal nerve fiber layer (pRNFL) thickness, macular volume, macular thickness (MT), foveal thickness, ganglion cell and inner plexiform layer thickness (GCL+IPL), cup volume, and cup/disc ratio (C/D) in the right and/or left eyes and/or the pRNFL and MT quadrants between patients with SSD and HC. Search databases were MEDLINE, Web of Science, PsycINFO, Cochrane Central, and medrxiv.org. Risk of bias was assessed with the Newcastle-Ottawa Scale. Standardized mean differences (SMD), subgroup analysis, and meta-regression with several variables were computed using the dmetar package in R. PROSPERO: CRD42021287873. STUDY RESULTS: Data from 22 reports (942 patients, 742 HC) were included. We found a retinal thinning in pRNFL (-0.30; 95% CI: -0.46, -0.14), macula (-0.37; 95% CI: -0.61, -0.13), and GCL+IPL (-0.33; 95% CI: -0.57, -0.10). The retinal thinning was especially pronounced in the superior and inferior quadrants of the inner ring of the macula. We also observed a decrease of macular volume (-0.44; 95% CI: -0.68, -0.20) and an increase in C/D ratio (0.35; 95% CI: 0.03, 0.67). CONCLUSIONS: Current evidence demonstrates retinal thinning in SSD, affecting both axonal and cellular structures, specially focused in the inner ring of the macula.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.027 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".