Colour vision defects in schizophrenia spectrum disorders: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it