Retinal Nerve Fiber Layer Thickness in Patients with Schizophrenia and Its Relation with Cognitive Impairment
Bibliographic record
Abstract
Background: Schizophrenia is a chronic severe mental illness with heterogeneous clinical presentation, course, and outcome. Cognitive impairment is one of its core features. Retinal nerve fiber layer (RNFL) imaging using OCT (optical coherence tomography) could provide easy access for in vivo imaging of the retina, rendering it as a “window to the brain.” Studies done on schizophrenia have shown RNFL thinning. This study attempts to look into the association between cognitive impairment, disease duration, and RNFL abnormality in patients with schizophrenia using OCT. Methods: Patients diagnosed with schizophrenia meeting DSM 5 (Diagnostic and Statistical Manual of Mental Disorders) criteria and who were confirmed to be in remission for at least six months clinically and scoring less than three on PANSS-8 (positive and negative symptom scale-8) remission scale were included. They were administered the Montreal Cognitive Assessment Scale (MoCA) for cognitive assessment. RNFL measures were taken using spectral domain-OCT. Variables were compared using Pearson’s correlation test, one-way ANOVA test, and independent t-test as appropriate. Results: A total of 36 patients were studied. MoCA scores and RNFL thickness showed a positive correlation. Patients with schizophrenia had reduced average RNFL thickness and reduced RNFL thickness in superior, inferior, and temporal quadrants. Average RNFL thickness, Superior and inferior quadrant RNFL thickness showed a positive correlation with MoCA scores. No correlation was obtained between macular volume, macular thickness, duration of illness, and MoCA scores. Conclusion: Patients with schizophrenia have reduced average RNFL thickness. Patients with low MoCA scores have RNFL thinning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".