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Record W2107399712 · doi:10.3310/hta14330

Cross-trimester repeated measures testing for Down’s syndrome screening: an assessment

2010· article· en· W2107399712 on OpenAlexaffabout
D. Wright, Ian Bradbury, Fergal D. Malone, Mary E. D’Alton, Anne Summers, Tianhua Huang, Susan Ball, Amanda W. Baker, Barry Nix, D.A. Aitken, Jenny Crossley, H. Cuckle, Kevin Spencer

Bibliographic record

VenueHealth Technology Assessment · 2010
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsNorth York General Hospital
FundersHealth Technology Assessment ProgrammeNational Institute for Health and Care Research
KeywordsMedicineObstetrics

Abstract

fetched live from OpenAlex

OBJECTIVES: To provide estimates and confidence intervals for the performance (detection and false-positive rates) of screening for Down's syndrome using repeated measures of biochemical markers from first and second trimester maternal serum samples taken from the same woman. DESIGN: Stored serum on Down's syndrome cases and controls was used to provide independent test data for the assessment of screening performance of published risk algorithms and for the development and testing of new risk assessment algorithms. SETTING: 15 screening centres across the USA, and at the North York General Hospital, Toronto, Canada. PARTICIPANTS: 78 women with pregnancy affected by Down's syndrome and 390 matched unaffected controls, with maternal blood samples obtained at 11-13 and 15-18 weeks' gestation, and women who received integrated prenatal screening at North York General Hospital at two time intervals: between 1 December 1999 and 31 October 2003, and between 1 October 2006 and 23 November 2007. INTERVENTIONS: Repeated measurements (first and second trimester) of maternal serum levels of human chorionic gonadotrophin (hCG), unconjugated estriol (uE3) and pregnancy-associated plasma protein A (PAPP-A) together with alpha-fetoprotein (AFP) in the second trimester. MAIN OUTCOME MEASURES: Detection and false-positive rates for screening with a threshold risk of 1 in 200 at term, and the detection rate achieved for a false-positive rate of 2%. RESULTS: Published distributional models for Down's syndrome were inconsistent with the test data. When these test data were classified using these models, screening performance deteriorated substantially through the addition of repeated measures. This contradicts the very optimistic results obtained from predictive modelling of performance. Simplified distributional assumptions showed some evidence of benefit from the use of repeated measures of PAPP-A but not for repeated measures of uE3 or hCG. Each of the two test data sets was used to create new parameter estimates against which screening test performance was assessed using the other data set. The results were equivocal but there was evidence suggesting improvement in screening performance through the use of repeated measures of PAPP-A when the first trimester sample was collected before 13 weeks' gestation. A Bayesian analysis of the combined data from the two test data sets showed that adding a second trimester repeated measurement of PAPP-A to the base test increased detection rates and reduced false-positive rates. The benefit decreased with increasing gestational age at the time of the first sample. There was no evidence of any benefit from repeated measures of hCG or uE3. CONCLUSIONS: If realised, a reduction of 1% in false-positive rate with no loss in detection rate would give important benefits in terms of health service provision and the large number of invasive tests avoided. The Bayesian analysis, which shows evidence of benefit, is based on strong distributional assumptions and should not be regarded as confirmatory. The evidence of potential benefit suggests the need for a prospective study of repeated measurements of PAPP-A with samples from early in the first trimester. A formal clinical effectiveness and cost-effectiveness analysis should be undertaken. This study has shown that the established modelling methodology for assessing screening performance may be optimistically biased and should be interpreted with caution.

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)0.196
metaresearch head score (Gemma)0.326
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.991

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1960.326
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0020.001
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 head0.067
GPT teacher head0.445
Teacher spread0.378 · 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.

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

Citations89
Published2010
Admission routes2
Has abstractyes

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