Glutamatergic metabolite correlations with neuropsychological tests in first episode schizophrenia
Bibliographic record
Abstract
Increased glutamatergic metabolites have been found in first episode schizophrenia. Although abnormal neuropsychological functioning has been demonstrated to be a core feature of schizophrenia, no studies have examined glutamatergic metabolites and neuropsychological function in drug-naive patients. The present study addressed whether higher levels of glutamatergic metabolites would be associated with poorer neuropsychological performance and social functioning in first episode patients. Glutamatergic concentration estimates were obtained from the left anterior cingulate cortex (ACC) and thalamus at baseline and 10 months after treatment in 16 patients with psychosis using 4.0 T (1)H magnetic resonance spectroscopy. A neuropsychological test battery was administered at baseline and 1 year. In the ACC, baseline glutamine was associated with performance on the Paced Auditory Serial Addition Task (PASAT). Glutamate at 10 months was associated with Wisconsin Card Sorting Test (WCST) errors and Trail-Making Test-B duration. Glutamine at 10 months was positively associated with WCST errors and negatively associated with WCST categories completed. In the thalamus, baseline glutamine was negatively associated with performance on the PASAT. Thalamic glutamate at baseline showed a trend towards a negative association with social functioning at 5 years. Glutamatergic metabolites were associated with neuropsychological test deficits and impaired social functioning at 5-year follow-up in patients with first episode psychosis, findings suggestive of an association between glutamatergic alterations on neurotoxicity early in the course of schizophrenia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".