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Record W1971471433 · doi:10.1159/000253122

Assessing the Potential Success of Cystic Fibrosis Carrier Screening: Lessons Learned from Tay-Sachs Disease and β-Thalassemia

2009· article· en· W1971471433 on OpenAlexaff
A.-M. Laberge, Carolyn Watts, Kyle Porter, Wylie Burke

Bibliographic record

VenuePublic Health Genomics · 2009
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersJohns Hopkins University
KeywordsMedicinePopulationThalassemiaDiseaseCarrier testingCystic fibrosisNewborn screeningPediatricsEnvironmental healthInternal medicinePrenatal diagnosisGeneticsBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to identify factors involved in the success of 2 well-established population-based carrier screening programs - Tay-Sachs disease (TSD) in Ashkenazi Jews and beta-thalassemia in Sardinia and Cyprus - and to assess the potential for success of a population-based cystic fibrosis (CF) carrier screening strategy using these factors. METHODS: We performed a literature review and key informant interviews. RESULTS: Factors involved in the success of TSD and beta-thalassemia carrier screening programs include disease characteristics (well-defined population at risk, severe disease with predictable course, availability of effective treatment), test characteristics (high sensitivity, straightforward interpretation of results), and community characteristics (involvement of community, support of families and advocacy groups, consensus in favor of avoiding affected births). Current CF screening strategies include few of the factors listed above. Unlike TSD and beta-thalassemia, the purpose of current CF carrier screening strategies is informed reproductive decision-making, without an explicit goal of reducing disease incidence. CONCLUSION: When compared to TSD and beta-thalassemia, CF is a less favorable candidate for population-based carrier screening. Because of its different purpose, CF carrier screening will require different measures of success than those used for TSD and beta-thalassemia carrier screening, and a consensus on the value or success of CF carrier screening may be difficult to achieve.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.394
Teacher spread0.316 · 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.

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

Citations13
Published2009
Admission routes1
Has abstractyes

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