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Record W4406810120 · doi:10.18280/ijsdp.200132

Factor Related to Maternal Mortality in Karawang District in Indonesia: Case-Control Study

2025· article· en· W4406810120 on OpenAlexvenueno aff
Raharni Raharni, Farah Dini Sabariti, Endang Indriasih, Made Ayu Lely Suratri, Rini Sasanti Handayani, Tati Suryati Warouw, Selma Siahaan

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsnot available
FundersUniversitas Airlangga
KeywordsWater resource managementGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Improving maternal health by reducing maternal mortality is one of the Millennium Development Goals.The goal is to significantly reduce maternal mortality.This study aimed to recognize risk factors for maternal mortality in Karawang Regency.A case-control study was conducted analyzing data from 108 maternal deaths (cases) and 216 pregnancies.Multiple logistic regression was used to identify significant risk factors.Research shows several risk factors for maternal death.Women aged 20 to 35 or older have a 2.55 times greater risk of death than women aged 20-34.Furthermore, delay in seeking help increased the risk by 6.21 times (ORA = 6.21, 95% CI = 2.17-17.77,P = 0.001).Furthermore, delay in coming to the community health center increases the risk by 5.35 times.This research highlights the importance of reducing maternal mortality rates in Karawang Regency, addressing the risks associated with increasing age, and reducing delays in seeking help and reaching health facilities.The finding of this research can help improve maternal health outcomes and form the basis of strategies to achieve the Millennium Development Goals.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.081
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.432
Teacher spread0.383 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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