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Record W4382940614 · doi:10.1016/j.ejmg.2023.104805

Lack of guidelines and translational knowledge is hindering the implementation of psychiatric genetic counseling and testing within Europe – A multi-professional survey study

2023· article· en· W4382940614 on OpenAlexaff
Kati Koido, Charlotta Ingvoldstad Malmgren, Lejla Pojskić, P.Z. Álmos, Sarah E. Bergen, Isabella Borg, Nada Božina, Domenico Coviello, Franziska Degenhardt, Lana Ganoci, Uffe Birk Jensen, Louise Durand-Lennad, Claudine Laurent‐Levinson, Andrew McQuillin, Alvydas Navickas, Nikolai Paul Pace, Milena Paneque, Marcella Rietschel, Maria Grigoroiu‐Serbânescu, Maria Soller, Jaana Suvisaari, Algirdas Utkus, E. Van Assche, Lily Vissouze, Shachar Zuckerman, Boris Chaumette, Kristiina Tammimies

Bibliographic record

VenueEuropean Journal of Medical Genetics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsMcGill University
FundersMinistarstvo Civilnih Poslova Bosne i HercegovineUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiEuropean Commission
KeywordsGenetic testingGeneticistPsychiatryGenetic counselingEuropean unionMedicineMental healthFamily medicinePsychologyClinical psychologyGenetics

Abstract

fetched live from OpenAlex

Genetic research has identified a large number of genetic variants, both rare and common, underlying neurodevelopmental disorders (NDD) and major psychiatric disorders. Currently, these findings are being translated into clinical practice. However, there is a lack of knowledge and guidelines for psychiatric genetic testing (PsychGT) and genetic counseling (PsychGC). The European Union-funded COST action EnGagE (CA17130) network was started to investigate the current implementation status of PsychGT and PsychGC across 35 participating European countries. Here, we present the results of a pan-European online survey in which we gathered the opinions, knowledge, and practices of a self-selected sample of professionals involved/interested in the field. We received answers from 181 respondents. The three main occupational categories were genetic counselor (21.0%), clinical geneticist (24.9%), and researcher (25.4%). Of all 181 respondents, 106 provide GC for any psychiatric disorder or NDD, corresponding to 58.6% of the whole group ranging from 43.2% in Central Eastern Europe to 66.1% in Western Europe. Overall, 65.2% of the respondents reported that genetic testing is offered to individuals with NDD, and 26.5% indicated the same for individuals with major psychiatric disorders. Only 22.1% of the respondents indicated that they have guidelines for PsychGT. Pharmacogenetic testing actionable for psychiatric disorders was offered by 15%. Interestingly, when genetic tests are fully covered by national health insurance, more genetic testing is provided for individuals with NDD but not those with major psychiatric disorders. Our qualitative analyses of responses highlight the lack of guidelines and knowledge on utilizing and using genetic tests and education and training as the major obstacles to implementation. Indeed, the existence of psychiatric genetic training courses was confirmed by only 11.6% of respondents. The question on the relevance of up-to-date education and training in psychiatric genetics on everyday related practice was highly relevant. We provide evidence that PsychGC and PsychGT are already in use across European countries, but there is a lack of guidelines and education. Harmonization of practice and development of guidelines for genetic counseling, testing, and training professionals would improve equality and access to quality care for individuals with psychiatric disorders within Europe.

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.148
GPT teacher head0.428
Teacher spread0.280 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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