[Clinical and neuropsychological correlates of proton magnetic resonance spectroscopy detected metabolites in brains of first-episode and schizophrenic patients].
Bibliographic record
Abstract
OBJECTIVE: This study examined 1H MRS detected metabolite levels (in left frontal, temporal lobes and thalamus) and clinical and cognitive features of patients with first-episode and chronic schizophrenia. METHOD: We studied 31 first-episode patients (group 1) and 17 chronic patients (group 2) with ICD-10 diagnosis of schizophrenia (and 13 healthy subjects). Patients were also assessed by the means of PANSS, CGI, Calgary scales and WCST, TMT, Stroop tests. RESULTS: We did not observe statistically significant differences in metabolite levels between group 1 and 2. We observed only a trend toward higher Cho level in temporal lobe in group 2 and lower NAA level in group 1. When comparing with the control group we observed a significantly higher Cho level in the frontal lobe (group 1,2) (p < 0.05). We observed a trend toward lower NAA levels in the frontal lobe (group 1,2), and lower NAA level in the temporal lobe (group 1). Patients with chronic schizophrenia performed significantly worse in WCST, TMT and Stroop tests (p < 0.05). CONCLUSION: These results suggest, that abnormalities in metabolite levels in frontal and temporal lobes are present at the onset of disease and don't progress over time. The cognitive dysfunction is more prominent in chronic patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".