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Record W2740431171 · doi:10.1371/journal.pmed.1002364

Counting stillbirths and achieving accountability: A global health priority

2017· article· en· W2740431171 on OpenAlexaff
Zulfiqar A Bhutta

Bibliographic record

VenuePLoS Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsSickKids FoundationCentre for Global Health ResearchHospital for Sick Children
Fundersnot available
KeywordsAccountabilityGlobal healthEnvironmental healthMedicinePublic healthPolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

Although the millennium development goals (MDGs) focused on maternal and child health and survival at inception, the importance of newborn survival to achievement of MDG 4 was soon well recognized [1].Over the last several years, the relative importance of stillbirths and their links to interventions to address maternal and newborn health have also been underscored [2].Despite this advocacy, the estimated 2.6 million stillbirths globally largely remain a hidden issue on the global policy platform, with little to no awareness for action at the country level [2].It took much effort towards the end of the MDG period to have stillbirth rates included as one of the 16 key indicators for monitoring progress for the global strategy for women, children, and adolescents [3].We need better data than modeled estimates to better define the burden and etiology of stillbirths from representative population-based studies or vital registration systems.The latter are relatively uncommon as the source of information in low-and middle-income settings.In this week's PLOS Medicine, Dandona and colleagues [4] underscore the importance of stillbirths in a population-based survey of Bihar (India).The study was based on verbal autopsies conducted on 1,132 stillbirths identified among 100,000 households over a 38-month period.Their identified incidence rate of stillbirths of 21.2 per 1,000 births (95% CI 19.7-22.6) is very close to the modeled estimated rate of 22 per 1,000 births [5].In a little over a third of these stillbirths, no cause could be identified, whereas obstetric complications and hemorrhage were associated with 30% of stillbirths.These findings are broadly consonant with the limited information on etiology from other studies of stillbirths [5,6].In a large verbal autopsy-based analysis of 1,285 stillbirths across a national sample of 95,000 households in the Pakistan demographic and health survey in 2006, 33.5% of antepartum stillbirths and 20.9% of intrapartum stillbirths did not have a clear cause of death [7].Many of the findings reported by Dandona et al [4] are well recognized risk factors for poor pregnancy outcomes, such as the association of stillbirths with poor quality of antenatal care (no antenatal care in 41% and 52% of urban or rural samples, respectively), small size of the fetus (25% and 38%), maternal fever in about 15%-16%, and evidence of infection in 15%-20% of subjects.No information is provided on the proportion of births among younger adolescents (<18 years), an important factor in the context of India, where adverse birth outcomes are associated with young maternal age [8].Similarly, we do not have information specific on access to caesarean section deliveries in the cohort, as limited access to emergency caesarean sections is a recognized risk factor for stillbirths at the population level [5].Given the lack of corresponding data on general births in Bihar or neonatal deaths, it is difficult to extrapolate the findings to beyond the stillbirth cohort.However, the risk factors are well known and do provide information relevant for policy.The authors relied on verbal narratives in their

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)0.083
metaresearch head score (Gemma)0.141
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.083
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.141
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0040.014
Scholarly communication0.0110.025
Open science0.0040.016
Research integrity0.0100.022
Insufficient payload (model declined to judge)0.0120.003

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 head0.034
GPT teacher head0.369
Teacher spread0.335 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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