Retinal Changes in Schizophrenia: A Systematic Review and Meta-analysis Based on Individual Participant Data
Bibliographic record
Abstract
BACKGROUND: Retinal assessment has indicated the presence of neuronal loss in neurodegenerative disorders, but its role in schizophrenia remains unclear. We sought to synthesize the available evidence considering 3 noninvasive modalities: optical coherence tomography, electroretinography, and fundus photography, and examine their diagnostic accuracy based on unpublished individual participant data, when provided by the primary study authors. METHODS: We searched MEDLINE, SCOPUS, clinicaltrials.gov, PSYNDEX, Cochrane Controlled Register of Trials (CENTRAL), WHO International Clinical Trials Registry Platform, and Google Scholar, up to October 30, 2018. Authors were contacted and invited to share anonymized participant-level data. Aggregate data were pooled using random effects models. Diagnostic accuracy meta-analysis was based on multiple cutoffs logistic generalized linear mixed modeling. This study was registered with PROSPERO, number CRD42018109344. RESULTS: Pooled mean differences of peripapillary retinal nerve fiber layer thickness in micrometer between 694 eyes of 432 schizophrenia patients and 609 eyes of 358 controls, from 11 case-control studies, with corresponding 95% confidence intervals (CIs) by quadrant were the following: -4.55, 95% CI: -8.28, -0.82 (superior); -6.25, 95% CI: -9.46, -3.04 (inferior); -3.18, 95% CI: -5.04, -1.31 (nasal); and -2.7, 95% CI: -4.35, -1.04 (temporal). Diagnostic accuracy, based on 4 studies, was fair to poor, unaffected by age and sex; macular area measurements performed slightly better. CONCLUSION: The notion of structural and functional changes in retinal integrity of patients with schizophrenia is supported with current evidence, but diagnostic accuracy is limited. The potential prognostic, theranostic, and preventive role of retinal evaluation remains to be examined.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".