Prenatal genetic counselling for psychiatric disorders
Bibliographic record
Abstract
Psychiatric disorders like schizophrenia, bipolar disorder, depression, anxiety, and obsessive-compulsive disorder are common disorders with complex aetiology. They can exact a heavy toll on the individual with the condition and can have significant impact on family members too. Accordingly, psychiatric disorders can arise as a concern in the prenatal context - couples may be interested in learning about the chance for their child to develop the illness that manifests in the family and may be interested in discussing options for prenatal testing. However, the complex nature of these conditions can present challenges for clinicians who seek to help families with these issues. We established the world's first specialist genetic counselling service of its kind in Vancouver, Canada, in 2012, and to date, have provided counselling for ~500 families and have demonstrated increases in patients' empowerment and self efficacy after genetic counselling. We draw on our accumulated clinical experience to outline the process by which we approach prenatal genetic counselling for psychiatric disorders to assist other clinicians in providing thoughtful, comprehensive support to couples seeking out this service. © 2016 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".