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Record W2127835015 · doi:10.3109/0167482x.2012.757590

Preparation for prenatal decision-making: a baseline of knowledge and reflection in women participating in prenatal screening

2013· article· en· W2127835015 on OpenAlexaboutno aff
Judith L. M. McCoyd

Bibliographic record

VenueJournal of Psychosomatic Obstetrics & Gynecology · 2013
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePregnancyQuarter (Canadian coin)Prenatal carePrenatal diagnosisMedical diagnosisAnxietyObstetricsBaseline (sea)FetusFamily medicineGynecologyPsychiatryPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: This prospective study gathered baseline information about knowledge and intentions regarding prenatal testing from women attending their nuchal translucency (NT) ultrasound and first sequential blood screen. METHOD: Surveys including questions about pregnancy history and hypotheticals about fetal diagnoses were distributed to all willing participants at an urban hospital and a suburban medical building during the waiting time for the NT (N = 659). RESULTS: The majority "never thought anything could be wrong" with their fetus and had not talked with the father or the health providers about that possibility. Presented with varied fetal diagnoses, the larger group nearly always "had the baby", except in the case of a fatal condition where 28% said they would have the baby in contrast to 26% who would end the pregnancy (remainder undecided). Hypotheticals about varied fetal conditions were generally "undecided" by a quarter to nearly half of the respondents. CONCLUSION: Women's baseline knowledge and reflection about the nature of prenatal screening and diagnosis are minimal in contrast to the large impact positive results could have on their lives. Providers need to weigh the benefit of priming decision-making by exposing women to the possibility of fetal diagnosis, against the cost of raised anxiety.

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)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.360
Teacher spread0.333 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Psychosomatic Obstetrics & GynecologySame topicPrenatal Screening and DiagnosticsFrench-language works237,207