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Record W1995540117 · doi:10.1111/cge.12418

Cross‐border reprogenetic services

2014· review· en· W1995540117 on OpenAlexafffund
Vincent Couture, Régen Drouin, Seang Lin Tan, Chantal Bouffard

Bibliographic record

VenueClinical Genetics · 2014
Typereview
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsInstitut National d'Excellence en Santé et en Services SociauxMcGill UniversityOttawa Fertility CentreUniversité de Sherbrooke
FundersUniversité de Sherbrooke
KeywordsScope (computer science)PhenomenonService (business)Economic JusticePolitical scienceCorporate governancePublic relationsNarrativeBusinessMedicineLawComputer scienceMarketing

Abstract

fetched live from OpenAlex

The purpose of this review is to synthesize the current knowledge on the international movement of patients and biopsied embryo cells for pre-implantation genetic diagnosis and its different applications. Thus far, few attempts have been made to identify the specific nature of this phenomenon called 'cross-border reprogenetic services'. There is scattered evidence, both empirical and speculative, suggesting that these services raise major issues in terms of service provision, risks for patients and the children-to-come, the legal liabilities of physicians, as well as social justice. To compile this evidence, this review uses the narrative overview protocol combined with thematic analysis. Five major themes have emerged from the literature at the conjunction of cross-border treatments and reprogenetics: 'scope', 'scale', 'motivations', 'concerns', and 'governance'. Similar themes have already been observed in the case of other medical tourism activities, but this review highlights their singularity with reprogenetic services. It emphasizes the diagnostic and autologous feature of reprogenetics, the constant risk of misdiagnosis, the restriction on certain tests for medically controversial conditions, and the uncertain accessibility of genetic counseling in cross-border settings.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.190
GPT teacher head0.598
Teacher spread0.408 · 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 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

Citations7
Published2014
Admission routes2
Has abstractyes

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