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Record W2039205448 · doi:10.1080/10408360390247832

Carrier Testing for Autosomal- Recessive Disorders

2003· review· en· W2039205448 on OpenAlexaff
Hilary Vallance, Jason C. Ford

Bibliographic record

VenueCritical Reviews in Clinical Laboratory Sciences · 2003
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsCarrier testingPopulationGenetic counselingDiseaseNewborn screeningPrenatal diagnosisMedicineGenetic testingMedical geneticsGeneticsPediatricsBiologyPathologyInternal medicinePregnancyEnvironmental health

Abstract

fetched live from OpenAlex

The aim of carrier testing is to identify carrier couples at risk of having offspring with a serious genetic (autosomal recessive) disorder. Carrier couples are offered genetic consultation where their reproductive options, including prenatal diagnosis, are explained. The Ashkenazi Jewish population is at increased risk for several recessively inherited disorders (Tay-Sachs disease, Cystic fibrosis, Canavan disease, Gaucher disease, Familial Dysautonomia, Niemann-Pick disease, Fanconi anemia, and Bloom syndrome). Unlike Tay-Sachs disease, there is no simple biochemical or enzymatic test to detect carriers for these other disorders. However, with the rapid identification of disease-causing genes in recent years, DNA-based assays are increasingly available for carrier detection. Approximately 5% of the world's population carries a mutation affecting the globin chains of the hemoglobin molecule. Among the most common of these disorders are the thalassemias. The global birth rate of affected infants is at least 2 per 1000 (in unscreened populations), with the greatest incidence in Southeast Asian, Indian, Mediterranean, and Middle Eastern ethnic groups. Carriers are detected by evaluation of red cell indices and morphology, followed by more sophisticated hematological testing and molecular analyses. The following issues need to be considered in the development of a carrier screening program: (1) test selection based on disease severity and test accuracy; (2) funding for testing and genetic counselling; (3) definition of the target population to be screened; (4) development of a public and professional education program; (5) informed consent for screening; and (6) awareness of community needs.

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)0.012
metaresearch head score (Gemma)0.343
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.343
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.004
Science and technology studies0.0000.004
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 head0.276
GPT teacher head0.568
Teacher spread0.291 · 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

Citations57
Published2003
Admission routes1
Has abstractyes

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