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Record W4400615392 · doi:10.22374/cjmrp.v10i1.113

Vaginal Birth After Cesarean Section: Outcomes of Women Receiving Midwifery Care in Ontario

2024· article· en· W4400615392 on OpenAlexfundaboutno aff
Elizabeth K. Darling

Bibliographic record

VenueCanadian Journal of Midwifery Research and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
FundersOntario Ministry of Health and Long-Term Care
KeywordsVaginal birthObstetricsSection (typography)MedicinePregnancyGynecologyComputer science

Abstract

fetched live from OpenAlex

Providing care to women with a history of cesarean section is within the scope of practice of Canadian midwives, and midwifery care may be of benefit to women who plan a vaginal birth after cesarean section. This retrospective cohort study describes the birth outcomes of women with a history of cesarean section cared for by a midwife in Ontario between April 1, 2003 and March 31, 2008 (n= 3262). The primary outcome was cesarean section, and the secondary outcome was perinatal mortality. The overall rate of cesarean section in this cohort was 46.1%, but among women who laboured the rate of cesarean section was 28.8%. There was not a statistically significant difference in perinatal mortality (excluding congenital anomalies and stillbirth prior to labour) when women with a history of cesarean section (0.18%) were compared to those without (0.20%), p=0.99. This study demonstrates positive outcomes for both mothers and babies when midwives are primary care providers during the intrapartum period for women with a history of cesarean section. There is a need to explore the factors contributing to the high rate of planned repeat cesarean section in this cohort.

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)0.000
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 score0.077
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.082
GPT teacher head0.405
Teacher spread0.324 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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