Psychiatric genetic counseling for people with copy number variants associated with psychiatric conditions
Bibliographic record
Abstract
22q11.2 deletion is one of the most well-known copy number variants (CNVs) associated with developing a psychiatric condition (e.g., schizophrenia), but there is a growing list of other CNVs which also confer substantial risk for developing psychiatric conditions. With increased use of chromosome microarray and exome sequencing, the frequency with which these CNVs are detected is increasing. While individuals with such CNVs often receive genetic counseling, research shows that associated psychiatric conditions are less often addressed-clinicians tend to focus on the nonpsychiatric manifestations of the CNV. This represents an important service gap for people with these CNVs and their families, as research shows that not only do these families want genetic counseling about psychiatric illness, it can also produce meaningful positive outcomes for people, including increases in empowerment, and self-efficacy. Therefore, there is a need to ensure that individuals with psychiatric condition-associated CNVs are being counseled about these manifestations of their condition in a way that can promote the best outcomes. In this paper we describe the process of providing genetic counseling in two clinical scenarios in which a psychiatric susceptibility CNV is identified: (1) in an individual who has not been diagnosed with a psychiatric condition and (2) in an individual with an established psychiatric condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".