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Genetic counselling in prenatally diagnosed non-chromosomal fetal abnormalities

2000· review· en· W2314755135 on OpenAlexaff
David Chitayat, Riyana Babul‐Hirji

Bibliographic record

VenueCurrent Opinion in Obstetrics & Gynecology · 2000
Typereview
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsHospital for Sick ChildrenToronto Public HealthMount Sinai Hospital
Fundersnot available
KeywordsMedicineGenetic counselingPrenatal diagnosisEtiologyFetusPregnancyObstetricsBioinformaticsPathologyGenetics

Abstract

fetched live from OpenAlex

Advances in technology and skills have resulted in the improved detection of fetal ultrasound abnormalities by ultrasound. In addition, the development of new diagnostic methods has resulted in major advances in our ability to detect microscopic and submicroscopic chromosome abnormalities as well as single gene disorders. This often enables us to provide the family with accurate information regarding the aetiology, prognosis, the risk of recurrence and the prenatal diagnosis options available in future pregnancies. Genetic counselling is important because this information should be communicated to the family in simple language, with care and sensitivity, so that the family can make decisions that are fully informed.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.065
GPT teacher head0.357
Teacher spread0.292 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations13
Published2000
Admission routes1
Has abstractyes

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