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Record W4285493100 · doi:10.1002/uog.26030

Impact of rural residence and low socioeconomic status on rate and timing of prenatal detection of major congenital heart disease in a jurisdiction of universal health coverage

2022· article· en· W4285493100 on OpenAlexaffabout
A. Kaur, Lisa K. Hornberger, Deborah Fruitman, Deliwe P. Ngwezi, Luke Eckersley

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2022
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsMedicineResidencePrenatal diagnosisSocioeconomic statusPrenatal carePediatricsGreat arteriesObstetricsHeart diseaseDemographyPregnancyFetusPopulationEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Socioeconomic status (SES) and distance of residence from tertiary care may impact fetal detection of congenital heart disease (CHD), partly through reduced access to and quality of obstetric ultrasound screening. It is unknown whether SES and remoteness of residence (RoR) affect prenatal detection of CHD in jurisdictions with universal health coverage. We examined the impact of SES and RoR on the rate and timing of prenatal diagnosis of major CHD within the province of Alberta in Canada. METHODS: In this retrospective study, we identified all fetuses and infants diagnosed with major CHD in Alberta, from 2008 to 2018, that underwent cardiac surgical intervention within the first year after birth, died preoperatively, were stillborn or underwent termination. Using maternal residence postal code and geocoding, Chan SES index quintile, geographic distance from a tertiary-care fetal cardiology center and the Canadian Index of Remoteness (IoR) were calculated. Outcome measures included rates of prenatal diagnosis and diagnosis after 22 weeks' gestation. Risk ratios (RR) were calculated using log-binomial regression and stratified by rural (≥ 100 km from tertiary care) or metropolitan (< 100 km from tertiary care) residence, adjusting for year of birth and the obstetric ultrasound screening view in which CHD would most likely be detected (four-chamber view; outflow-tract view; three-vessel or three-vessels-and-trachea or non-standard view; septal view). RESULTS: Of 1405 fetuses/infants with major CHD, prenatal diagnosis occurred in 814 (57.9%). Residence ≥ 100 km from tertiary care (adjusted RR, 1.19; 95% CI, 1.05-1.34) and higher IoR (adjusted RR, 1.9; 95% CI, 1.1-3.3) were associated with missed prenatal diagnosis of major CHD. Similarly, residence ≥ 100 km from tertiary care (adjusted RR, 1.41; 95% CI, 1.22-1.62) and higher IoR (adjusted RR, 3.6; 95% CI, 2.2-8.2) were associated with prenatal diagnosis after 22 weeks. Although adjusted and unadjusted analyses showed no association between Chan SES index quintile and prenatal-diagnosis rate overall nor for residence in rural areas, in metropolitan regions, lower SES quintiles were associated with missed prenatal diagnosis (quintile 1: RR, 1.24; 95% CI, 1.02-1.50) and higher risk of diagnosis after 22 weeks' gestation (quintile 1: RR, 1.46; 95% CI, 1.10-1.93; quintile 2: RR, 1.66; 95% CI, 1.24-2.23). CONCLUSIONS: Despite universal healthcare, rural residence in Alberta is associated with lower rate of prenatal diagnosis of major CHD and higher risk of late prenatal diagnosis (≥ 22 weeks). Within metropolitan regions, lower SES impacts negatively prenatal-diagnosis rate and timing. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.762
Threshold uncertainty score0.479

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.267
Teacher spread0.259 · 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.

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

Citations31
Published2022
Admission routes2
Has abstractyes

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