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Record W3135088026 · doi:10.29063/ajrh2021/v25i1.15

First trimester antenatal care visit reduces the risk of miscarriage among women of reproductive age in Ghana.

2021· article· en· W3135088026 on OpenAlexaff
Batholomew Chireh, Samuel Kwaku Essien, Carl D’Arcy

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsSaskatchewan Health AuthorityUniversity of SaskatchewanSaskatchewan Health Quality Council
Fundersnot available
KeywordsMiscarriageMedicineObstetricsPregnancyPoisson regressionAbortionFirst trimesterReproductive healthGynecologyPopulationEnvironmental healthGestation

Abstract

fetched live from OpenAlex

Miscarriage is a common adverse pregnancy outcome in childbearing and an increasing global reproductive health problem. This study explored 1) the national prevalence of the first trimester (≤12 weeks) miscarriage among women (15-49 years) in Ghana, and 2) the influence of first-trimester antenatal care (ANC) visits on miscarriage risk. A cross-sectional study using the Demographic Health Survey (DHS- 2017) on maternal health in Ghana was conducted. We used a nationally representative subsample of (7,846) women with no or early ANC visit of the initial sample (25,062). Women with late ANC visit (≥12 weeks) and those who were never pregnant or had not given birth at the time of the survey were excluded from this analysis. We performed multivariable Poisson regression to estimate miscarriage risk (RR), its associated risk factors, and national prevalence. The national first-trimester miscarriage prevalence was 19.1%. Increasing maternal age and urban residence were significantly associated with the risk of first- trimester miscarriage (p <0.001) while early ANC visits lower the risk of miscarriage by 43% (p=0.0246). We found that first trimester ANC visit decreases miscarriage risk in Ghana and highlights the important role of early ANC visits in reducing miscarriages.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.116
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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.

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

Citations7
Published2021
Admission routes1
Has abstractyes

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