Evaluating Genetic Counseling for Individuals With Schizophrenia in the Molecular Age
Bibliographic record
Abstract
BACKGROUND: Recent advances in schizophrenia genetics are shedding new light on etiopathogenesis, but issues germane to translation of findings into clinical practice are relatively understudied. We assessed the need for, and efficacy of, a contemporary genetic counseling protocol for individuals with schizophrenia. METHODS: After characterizing rare copy number variation in a cohort of adults with schizophrenia, we recruited subjects from the majority of individuals who had no clinically relevant structural genetic variant. We used a pre-post study design with longitudinal follow-up to assess both the profile of need and the impact of general genetic counseling on key knowledge-based and psychological factors. RESULTS: Thirty-nine (60.0%) of 65 patients approached actively expressed an interest in the study. At baseline, participants (n = 25) tended to overestimate the risk of familial recurrence of schizophrenia, express considerable concern related to this perceived risk, endorse myths about schizophrenia etiology, and blame themselves for their illness. Postcounseling, there was a significant improvement in understanding of the empiric recurrence risk (P = .0090), accompanied by a decrease in associated concern (P = .0020). There were also significant gains in subjective (P = .0007) and objective (P = .0103) knowledge, and reductions in internalized stigma (P = .0111) and self-blame (P = .0401). Satisfaction with genetic counseling, including endorsement of the need for such counseling (86.4%), was high. CONCLUSIONS: These results provide initial evidence of need for, and efficacy of, genetic counseling for individuals with schizophrenia. The findings may help facilitate development of a contemporary genetic counseling process that could optimize outcomes in the nascent field of evidence-based psychiatric genetic counseling.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".