Colour vision defects in schizophrenia spectrum disorders: A systematic review
Bibliographic record
Abstract
This systematic review synthesized the existing literature to summarize colour vision disturbances experienced by patients with schizophrenia. A comprehensive literature search compliant with PRISMA-2020 was conducted in Medline and Embase from inception to February 28, 2023. Studies were included if they: (1) included people diagnosed with schizophrenia, (2) investigated colour vision, (3) had a comparator with or without schizophrenia. Study quality appraisal was performed using the NIH Study Quality Assessment Tool. Seven studies of fair quality with 695 patients were included, of whom, 46.5% (n = 323) patients were diagnosed with a schizophrenia-spectrum disorder. Compared to healthy controls, patients with schizophrenia either made more mistakes in discriminating between colours, or were delayed in recognizing colours. One study found that Positive and Negative Syndrome Scale for Schizophrenia (PANSS) scores correlated weakly with error scores related to colour vision impairments. The most common shortcomings were lack of sample size justification (k = 7, 100%), and lack of blinding (k = 7, 100%). Our review indicates early evidence of colour vision deficits among patients with schizophrenia, and an unclear relationship between severity of schizophrenia with colour vision deficits. Possible mechanisms may include alterations in retinal dopamine transmission or schizophrenia-related cognitive deficits interacting with colour vision outcomes. Future studies may benefit from large registry analyses of patients with various schizophrenia spectrum disorders, analyzing ocular parameters (e.g., OCT), collecting data on cognitive impairment, and pursuing multivariate analyses to elucidate mechanisms for schizophrenia-related colour vision changes.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".