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Preimplantation Genetic Diagnosis: An Overview of Socio-Ethical and Legal Considerations

2006· review· en· W2127666181 on OpenAlexaff
Bartha Maria Knoppers, Sylvie Bordet, Rosario Isasi

Bibliographic record

VenueAnnual Review of Genomics and Human Genetics · 2006
Typereview
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPreimplantation genetic diagnosisSex selectionContext (archaeology)WarrantEthical issuesGenetic diagnosisSelection (genetic algorithm)Genetic testingPolitical scienceEngineering ethicsSociologyEmbryoBiologyBusinessGeneticsDemographyEngineering

Abstract

fetched live from OpenAlex

Preimplantation genetic diagnosis (PGD) permits the selection of embryos of a particular genotype prior to implantation. As a reproductive technology involving embryo selection, PGD has become associated with considerable controversy. This review examines some of the ethical, legal, and social issues raised by PGD. Relevant ethical considerations include the status of the embryo and the interests and duties of the parents. On a social policy level, considerations of access as well as the impact of this technology on families, women, and physician's duties also warrant consideration. An analysis of these issues in the context of using PGD for selecting embryos unaffected by a serious disorder and for sex selection is presented. We also present a brief survey of PGD-related regulatory schemes in several countries, including the United Kingdom and the United States.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.085
GPT teacher head0.402
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations83
Published2006
Admission routes1
Has abstractyes

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