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Record W2299679120 · doi:10.14288/1.0045274

Triple-marker screening in British Columbia : current practice, future options : final report made to the Minister's Advisory Council on Women's Health

2014· article· en· W2299679120 on OpenAlexaboutno aff
Kenneth Bassett, Patricia M. Lee, C. J. Green, Lisa Mitchell, Hana Sroka, Rohinee Lal, Robin Hanvelt, Arminée Kazanjian, Garry Fletcher

Bibliographic record

VenuecIRcle (University of British Columbia) · 2014
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceAdvisory committeePublic administrationMedicine

Abstract

fetched live from OpenAlex

The purpose of this review is to examine maternal serum triple-marker screening (TMS) for fetuses with Down syndrome, other chromosomal abnormalities, and spina bifida, in the British Columbia context. Evaluation is both qualitative, addressing the personal and social significance of TMS, and quantitative, assessing effectiveness and cost. The review is organized around the evaluation of four possible options for funding TMS. The first three options are in essence variations on current TMS practice in the province. A fourth option adds co-ordination of TMS, that is, provision, standardization, evaluation, and education throughout the province. In the interest of clarity, the options are discussed as much as possible in relation to Down syndrome, the most common condition traced through TMS. The review has been conducted with particular attention to the interests of groups who may in some respects be seen as vulnerable, women during pregnancy and members of the Down syndrome community; and also to the concerns of TMS providers. The issues examined are those relevant to large-scale population screening of women considered at low pre-test risk of carrying an affected fetus. Not addressed are issues of particular relevance to women identified as being at high pre-test risk owing to individual or family history of affected births.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.323
Teacher spread0.228 · 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
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

Citations0
Published2014
Admission routes1
Has abstractyes

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