Divergent backward masking performance in schizophrenia and bipolar disorder: Association with COMT
Bibliographic record
Abstract
Schizophrenia has been reliably associated with impairments in backward masking performance, while bipolar disorder has less consistently been tied to such a deficit. To examine the genetic determinants of visual perception abnormalities in schizophrenia and bipolar disorder, this study evaluated the diagnostic specificity of backward masking performance deficits and whether masking deficits were associated with catechol-O-methyl transferase (COMT) genotype. A location-based backward masking task, which equated participants on the perceptual intensity of stimuli, was completed by 41 schizophrenia outpatients, 28 bipolar outpatients, and 43 nonpsychiatric controls. COMT genotype data were available for 39 schizophrenia outpatients, 28 bipolar outpatients, and 20 nonpsychiatric controls. Schizophrenia patients demonstrated impaired backward masking performance compared to controls and bipolar patients. A group by COMT genotype interaction was detected with schizophrenia Met homozygotes performing more poorly than control and bipolar Met homozygotes, and worse than Val homozygote and heterozygote schizophrenia patients. This study provides novel evidence for differential effects of the COMT gene on neural systems underlying visual perception in schizophrenia and bipolar disorder. The COMT Met allele may be associated with deficits in schizophrenia that are unrelated to neural systems supporting sustained attention or working memory.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".