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Record W3122240480 · doi:10.1186/s12884-021-03545-7

Maternal alcohol use, adverse neonatal outcomes and pregnancy complications in British Columbia, Canada: a population-based study

2021· article· en· W3122240480 on OpenAlexafffundabout
Svetlana Popova, Danijela Dozet, Graham O’Hanlon, Valerie Temple, Jürgen Rehm

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2021
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsCanada Research ChairsPublic Health OntarioUniversity of TorontoSurrey Place CentreCentre for Addiction and Mental Health
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicinePregnancyRisk factorObstetricsPopulationReproductive medicinePediatricsEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The current study aimed to estimate the prevalence of alcohol use identified as a risk factor during pregnancies by the antenatal care providers, resulting in live births in British Columbia (BC) and to examine associations between alcohol use, adverse neonatal outcomes, and pregnancy complications. METHODS: This population-based cross-sectional study utilized linked obstetrical and neonatal records within the BC Perinatal Data Registry (BCPDR), for deliveries that were discharged between January 1, 2015 and March 31, 2018. The main outcome measures were alcohol use identified as a risk factor during pregnancy, associated maternal characteristics, pregnancy complications, and adverse neonatal outcomes. Estimates for the period and fiscal year prevalence were calculated. Chi-square tests were used to compare adverse neonatal outcomes and pregnancy complications by alcohol use during pregnancy identified as a risk factor. Logistic regression was used to examine the association between alcohol use identified as a risk factor during pregnancy and adverse neonatal outcomes and pregnancy complications, after adjusting for identified risk factors. RESULTS: A total of 144,779 linked records within the BCPDR were examined. The period prevalence of alcohol use during pregnancy identified as a risk factor was estimated to be 1.1% and yearly prevalence was 1.1, 1.1, 1.3 and 0.9% from the 2014/2015 to 2017/2018 fiscal years, respectively. Alcohol use identified as a risk factor was associated with younger maternal age, fewer antenatal visits, being primiparous, a history of mental illness, substance use and smoking. Neonates with alcohol use during pregnancy identified as a risk factor had greater odds of being diagnosed with: "low birth weight (1000-2499g)" (ICD-10: P07.1; aOR = 1.25; 95% CI: 1.01, 1.53), "other respiration distress of newborn" (ICD-10: P22.8; aOR = 2.57; 95% CI: 1.52, 4.07), "neonatal difficulty in breastfeeding" (ICD-10: P92.5; aOR = 1.97; 95% CI: 1.27, 2.92) and "feeding problems, unspecified" (ICD-10: P92.9; aOR = 2.06; 95% CI: 1.31, 3.09). CONCLUSIONS: The prevalence of alcohol use during pregnancy identified as a risk factor was comparable to previous estimates within the BCPDR. Identified prenatal alcohol exposure was associated with notable differences in maternal and neonatal characteristics and adverse neonatal outcomes. More consistent, thorough screening and prevention efforts targeting alcohol use in pregnancy are urgently needed in Canada.

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)0.002
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 score0.022
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations39
Published2021
Admission routes3
Has abstractyes

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