The retina across the psychiatric spectrum: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract The identification of structural retinal layer differences between patients diagnosed with certain psychiatric disorders and healthy controls has provided a potentially promising route to the identification of biomarkers for these disorders. Optical coherence tomography has been used to study whether retinal structural differences exist in schizophrenia spectrum disorders (SSD), bipolar disorder (BPD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), attention deficit hyperactivity disorder (ADHD), and alcohol and opiate use disorders. However, there is considerable variation in the amount of available evidence relating to each disorder and heterogeneity in the results obtained. We conducted the first systematic review and meta-analysis of evidence across all psychiatric disorders for which data was available. The quality of the evidence was graded and key confounding variables were accounted for. Of 381 screened articles, 87 were included. The evidence was of very low to moderate quality. Meta-analyses revealed that compared to healthy controls, the peripapillary retinal nerve fiber layer (pRNFL) was significantly thinner in SSD (SMD = -0.32; p<0.001), BPD (SMD = -0.4; p<0.001), OCD (SMD = -0.26; p=0.041), and ADHD (SMD = -0.48; p=0.033). Macular thickness was only significantly less in SSD (SMD = -0.59; p<0.001). pRNFL quadrant analyses revealed that reduced pRNFL thickness in SSD and BPD was most prominent in the superior and inferior quadrants. Macular subfield analyses indicated that BPD may have region-specific effects on retinal thickness. In conclusion, these findings suggest substantial retinal differences in SSD and BPD, reinforcing their potential as biomarkers in clinical settings.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".