The association of functional brain networks common to source monitoring with the symptoms of schizophrenia
Bibliographic record
Abstract
was conducted to analyze group differences.Results: A significant group by age interaction, was found in PFC glutamate (beta ¼ 0.01, SE ¼ 0.01, t ¼ -2.69, p ¼ 0.009) and Glx (beta ¼ -0.012, SE ¼ 0.01, t ¼ -2.412, p ¼ 0.017), whereby glutamate and Glx levels showed a more pronounced age-related decrease in FPE compared to HR (glutamate: beta ¼ 0.022, SE ¼ 0.01, t ¼ 2.695, p ¼ 0.021; Glx: beta ¼ 0.030, SE ¼ 0.01, t ¼ 2.599, p ¼ 0.027) and controls (beta ¼ 0.02, SE ¼ 0.01, t ¼ 2.654, p ¼ 0.023; Glx: beta ¼ 0.027, SE ¼ 0.01, t ¼ 2.555, p ¼ 0.030).There was a also a significant effect of group, but not of age nor sex, for GPC+PCh levels in the PFC (beta ¼ 0.01, SE ¼ 0.005, t ¼ 5.408 p ¼ 0.006), whereby GPC+PCh levels were higher in FPE (beta ¼ -0.01, SE ¼ 0.01, t ¼ -3.140, p ¼ 0.006) compared to controls.There were no significant group effects in the medial temporal lobe.Discussion: These results are consistent with previous studies suggesting that changes in glutamatergic neurotransmission in the prefrontal cortex according to age and illness stage may be involved in the pathophysiology of psychotic disorders [1,2,3].Likewise, finding increasing levels of neuroinflammation markers (GPC+PCh) [4] with age in patients with FPE adds to previous evidence supporting the role of inflammatory processes in the early stages of psychosis [5].
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".