Performance of Verbal Fluency as an Endophenotype in Patients with Familial versus Sporadic Schizophrenia and Their Parents
Bibliographic record
Abstract
What's the neurocognitive deficit as an endophenotype to familial schizophrenia? Here, we investigate the neurocognitive endophenotype in first-episode patients with familial schizophrenia (FS) and sporadic schizophrenia (SS), and their parents. 98 FS patients and their 105 parents; 190 SS patients and their 207 parents; 195 controls matched with patients, and 190 controls matched with the patients' parents, were assessed with the short version of the Wechsler Adult Intelligence Scale-Revised in China (WAIS-RC), the immediate and delayed logical memory tests from the Wechsler Memory Scale-Revised in China (WMS-RC), the Verbal Fluency Test (VFT), the Trail Making Test Parts A and B-Modified (TMA, TMB-M), and the Modified Wisconsin Card Sorting Test (WCST-M). The results showed that with age, gender, and education as covariates, after controlling for false discovery rates, the FS group and their parent group performed worse than the SS group and their parent group on VFT. No significant differences were found for other neurocognitive tests between the FS and SS patient groups, and their respective parent groups. Our findings suggest the patients with familial and sporadic schizophrenia and their respective parent groups may have a different genetic predisposition in relation to a cognitive endophenotype.
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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".