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Record W2089123634 · doi:10.1002/pd.1321

The influence of risk estimates obtained from maternal serum screening on amniocentesis rates

2005· article· en· W2089123634 on OpenAlexaffabout
Valerie Martina Mueller, Tianhua Huang, Anne Summers, Stephanie Winsor

Bibliographic record

VenuePrenatal Diagnosis · 2005
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsNorth York General HospitalMcMaster University
Fundersnot available
KeywordsAmniocentesisMedicineObstetricsPregnancyGynecologyPrenatal diagnosisFetusBiologyGenetics

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the influence of Down syndrome risk estimates obtained from maternal serum screening (MSS) on women's choices regarding amniocentesis. METHODS: Women who screened positive for Down syndrome by an Ontario MSS program between 1993 and 1998 were grouped on the basis of their risk estimate and ethnicity. Amniocentesis uptake rates between the groups were compared in order to determine how the MSS risk estimate influenced uptake. RESULTS: Analysis of 16 792 women showed that amniocentesis uptake rates increased as the estimated risk increased. Uptake in women < or = 35 was higher than that for older women (70% vs 60%, p = 0.001). Uptake in Caucasian and Asian women was higher than the uptake in Black women (67% vs 49%, p = 0.001). Women aged 35 years or older were more likely to proceed with amniocentesis if the MSS risk estimate was higher than their age-specific risk. CONCLUSION: The increase in amniocentesis rate paralleled the increase in MSS risk estimate for Down syndrome. Risk-specific amniocentesis rates are higher in women aged less than 35 years. Women aged 35 years or older whose risk estimate by MSS is lower than their age-specific risk are less likely to opt for amniocentesis.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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

Citations18
Published2005
Admission routes2
Has abstractyes

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