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Record W4387491235 · doi:10.1016/j.cjcpc.2023.10.002

Clinically Relevant Genetic Considerations for Patients With Tetralogy of Fallot

2023· article· en· W4387491235 on OpenAlexafffund
Anne S. Bassett, Miriam S. Reuter, Sarah Malecki, Candice Silversides, Erwin Oechslin

Bibliographic record

VenueCJC Pediatric and Congenital Heart Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMental Health Research CanadaToronto General HospitalUniversity of TorontoCentre for Addiction and Mental HealthUniversity Health Network
FundersCanadian Institutes of Health ResearchW. Garfield Weston FoundationWeston Family FoundationMcLaughlin Centre, University of Toronto
KeywordsTetralogy of FallotGenetic testingMedicinePhenotypePrecision medicinePopulationHuman geneticsClinical PracticeIntensive care medicineBioinformaticsInternal medicineGeneticsHeart diseaseBiologyPathology

Abstract

fetched live from OpenAlex

Genetic changes affect embryogenesis, cardiac and extracardiac phenotype, development, later onset conditions, and both short- and long-term outcomes and comorbidities in the increasing population of individuals with tetralogy of Fallot (TOF). In this review, we focus on current knowledge about clinically relevant genetics for patients with TOF across the lifespan. The latest findings for TOF genetics that are pertinent to day-to-day practice and lifelong management are highlighted: morbidity/mortality, cardiac/extracardiac features, including neurodevelopmental expression, and recent changes to prenatal screening and diagnostics. Genome-wide microarray is the first-line clinical genetic test for TOF across the lifespan, detecting relevant structural changes including the most common for TOF, the 22q11.2 microdeletion. Accumulating evidence illustrates opportunities for advances in understanding and care that may arise from genetic diagnosis at any age. We also glimpse into the near future when the multigenic nature of TOF will be more fully revealed, further enhancing possibilities for preventive care. Precision medicine is nigh.

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)0.000
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 score0.004
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.285
Teacher spread0.264 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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