Regional Cerebral Glucose Metabolism in Never-Medicated Patients with Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assess regional cerebral glucose metabolism in patients with schizophrenia who had never received antipsychotic medication and whose olfactory identification ability had been assessed. Two hypotheses were examined. First, the patients were compared with normal controls to determine whether differences in regional cerebral metabolism were apparent. Second, regional rates of metabolism were correlated with olfactory ability and the relation between them determined. METHODS: The patient (n = 26) and control (n = 32) subjects were scanned at rest using positron emission tomography (PET) after administration of 18F-fluorodeoxyglucose (FDG). In addition, the University of Pennsylvania Smell Identification Test was administered to each patient. RESULTS: Patients with schizophrenia had reduced rates of glucose metabolism in the right and left thalamus that reached significance if not corrected for multiple comparisons. However, if a Bonferroni correction was applied over the 27 regions of interest, the differences were not significant. Scores on the Smell Identification Test were negatively correlated with 8 regions of interest. When scores were analyzed using multiple regression, the left frontal cortex and the medial parietal cortex were significant predictors. CONCLUSIONS: The finding of reduced metabolism in the thalami is consistent with some of the previous literature, whereas the negative correlations between specific regions and olfactory function are not consistent with studies using activation paradigms.
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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 | 0.000 | 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) | 0.001 | 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".