Genetic Counseling for Schizophrenia: A Review of Referrals to a Provincial Medical Genetics Program From 1968 to 2007
Bibliographic record
Abstract
Although previous studies reported that a majority of individuals with schizophrenia and their family members are interested in genetic counseling, few in each group had ever been offered genetic counseling. Although some individuals with genetic syndromes that include schizophrenia have received psychiatric genetic counseling, genetic syndromes account for only a very small proportion of patients with schizophrenia. There are little data on the characteristics of individuals/families who have received psychiatric genetic counseling. This exploratory retrospective study examined the population of individuals referred for genetic counseling in British Columbia for a primary indication of nongenetic schizophrenia, and assessed trends in the annual number of such referrals over time. All charts in the database of the provincial medical genetics program were reviewed to obtain information on demographics, psychiatric diagnosis, discipline of referring physician, referred individual's and partner's (where applicable) family history, and pregnancy history. Data were analyzed using descriptive statistics. Between 1968 and 2007, 288 individuals were referred for a primary indication of schizophrenia. The proportion of referrals (69%) was highest for family members with an affected first-degree relative not for individuals personally affected (25%). Most referrals were for preconception counseling. Family physicians accounted for 69% of the referrals, whereas obstetricians accounted for 9% and psychiatrists 2%. These findings show that only a small proportion of individuals with schizophrenia and their family members receive referrals for psychiatric genetic counseling in British Columbia. Future strategies to improve access to genetic counseling include outreach (showing the need of such counseling) to both this population and to healthcare professionals, especially psychiatrists and obstetricians.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".