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Record W2972816632 · doi:10.1101/cshperspect.a036608

Evidence-Based Genetic Counseling for Psychiatric Disorders: A Road Map

2019· review· en· W2972816632 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2019
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsBC Mental Health & Substance Use ServicesUniversity of British Columbia
Fundersnot available
KeywordsPsychiatryGenetic counselingContext (archaeology)AutismPsychiatric geneticsAnxietySchizophrenia (object-oriented programming)PopulationGenetic testingPsychologyClinical psychologyDepression (economics)MedicineGenetics

Abstract

fetched live from OpenAlex

Psychiatric disorders, such as schizophrenia, depression, anxiety, and bipolar disorder, are common conditions that arise as a result of complex and heterogeneous combinations of genetic and environmental factors. In contrast to childhood neurodevelopmental conditions such as autism and intellectual disability, there are no clinical practice guidelines for applying genetic testing in the context of these conditions. But genetic counseling and genetic testing are not synonymous, and people who live with psychiatric disorders and their family members are often interested in what psychiatric genetic counseling can offer. Further, research shows that it can improve outcomes like empowerment for this population. Despite this, psychiatric genetic counseling is not yet routinely or widely offered. This review describes the state of the evidence about the process and outcomes of psychiatric genetic counseling, focusing on its clinical implications and remaining research gaps.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.397
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it