Shared Neurocognitive Dysfunctions in Young Offspring at Extreme Risk for Schizophrenia or Bipolar Disorder in Eastern Quebec Multigenerational Families
Bibliographic record
Abstract
BACKGROUND: Adult patients having schizophrenia (SZ) or bipolar disorder (BP) may have in common neurocognitive deficits. Former evidence suggests impairments in several neuropsychological functions in young offspring at genetic risk for SZ or BP. Moreover, a dose-response relation may exist between the degree of familial loading and cognitive impairments. This study examines the cognitive functioning of high-risk (HR) offspring of parents having schizophrenia (HRSZ) and high-risk offspring of parents having bipolar disorder (HRBP) descending from densely affected kindreds. METHODS: The sample consisted of 45 young offspring (mean age of 17.3 years) born to a parent having SZ or BP descending from large multigenerational families of Eastern Québec that are densely affected by SZ or BP and followed up since 1989. The offspring were administered a lifetime best-estimate diagnostic procedure (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [DSM-IV]) and an extensive standard neuropsychological battery. Raw scores were compared with age- and gender-matched controls. RESULTS: The offspring displayed differences in memory and executive functions when compared with controls. Moderate to large effect sizes (Cohen d) ranging from 0.65 to 1.25 (for IQ and memory) were observed. Several of the cognitive dysfunctions were present in both HRSZ and HRBP, even when considering DSM-IV clinical status. CONCLUSIONS: HRSZ and HRBP shared several aspects of their cognitive impairment. Our data suggest that the extremely high genetic and familial loading of these HRs may have contributed to a quantitatively increased magnitude of the cognitive impairments in both HR subgroups, especially in memory. These offspring at heightened risk present difficulties in processing information that warrant preventive research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".