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Record W1865418518 · doi:10.3402/gha.v8.28702

Prenatal arsenic exposure and drowning among children in Bangladesh

2015· article· en· W1865418518 on OpenAlex
Mahfuzar Rahman, Nazmul Sohel, Samar Kumar Hore, Mohammad Yunus, Abbas Bhuiya, Peter Kim Streatfield

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Health Action · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineArsenicEnvironmental healthPregnancyVerbal autopsyPoison controlArsenic poisoningIn uteroHazard ratioPediatricsDemographyCause of deathFetusConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

There is increasing concern regarding adverse effects of prenatal arsenic exposure on the neurodevelopment of children. We analyzed mortality data for children, who were born to 11,414 pregnant women between 2002 and 2004, with an average age of 5 years of follow-up. Individual drinking-water arsenic exposure during pregnancy was calculated using tubewell water arsenic concentration between last menstrual period and date of birth. There were 84 drowning deaths registered, with cause of death ascertained using verbal autopsy (International Classification of Diseases, 10th revision, codes X65-X70). The prenatal water arsenic exposure distribution was tertiled, and the risk of drowning mortality was estimated by Cox proportional hazard models, adjusted for potential confounders. We observed a significant association between prenatal arsenic exposure and drowning in children aged 1-5 years in the highest exposure tertile (HR=1.74, 95% CI: 1.03-2.94). This study showed that in utero arsenic exposure might be associated with excess mortality among children aged 1-5 years due to drowning.

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.

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)0.000
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 score0.072
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.337
Teacher spread0.299 · 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