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Record W109447978

Assessing Health-Related Quality-of-Life in Prenatal Diagnosis Comparing Chorionic Villi Sampling and Amniocentesis: A Technical Report

2000· preprint· en· W109447978 on OpenAlexaboutno aff
David Feeny, Marie Townsend, William Furlong, Darrell J. Tomkins, Gail Erlick Robinson, George W. Torrance, Patrick Mohide, Qinan Wang

Bibliographic record

VenueRePEc: Research Papers in Economics · 2000
Typepreprint
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsAmniocentesisChorionic villus samplingMedicineObstetricsPregnancyChorionic villiQuality of life (healthcare)Analysis of varianceGynecologyExact testPrenatal diagnosisFirst trimesterGestationSurgeryFetusInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objectives. To assess the health-related quality-of-life (HRQL) effects of chorionic villi sampling (CVS) and genetic amniocentesis (GA) prenatal diagnosis, including factors related to both the processes and the outcomes. Study Design. The HRQL of one hundred twenty six women participating in a randomized controlled clinical trial of CVS versus GA in Toronto and Hamilton, Ontario was assessed in four interviews at weeks 8, 13, 18, and 22 of pregnancy. Statistical analyses included analysis of variance, repeated measures analysis of covariance, chi-square, Fisher’s exact test, Student’s t-tests, and paired t-tests. Results. Utility scores for patients undergoing CVS exceeded those for GA patients at week 18 (p = 0.04). Utility scores for hypothetical health states did not differ significantly by trial arm. Conclusions. CVS results in slightly improved HRQL relative to GA during the second trimester of pregnancy. This advantage needs to be weighed against the high disutility patients attach to infrequent outcomes associated with pregnancy losses, equivocal diagnoses, and diagnostic inaccuracy.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
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 head0.139
GPT teacher head0.422
Teacher spread0.284 · 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 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

Citations4
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicPrenatal Screening and DiagnosticsFrench-language works237,207