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Record W2020591771 · doi:10.1080/19338240903390362

Manganese, Arsenic, and Infant Mortality in Bangladesh: An Ecological Analysis

2010· article· en· W2020591771 on OpenAlexaff
Nicola Cherry, Kashem Shaik, Corbett McDonald, Zafrullah Chowdhury

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsArsenicInfant mortalityManganeseMedicineEnvironmental healthIncidence (geometry)Mortality rateOdds ratioDemographyPopulationSurgeryChemistryPathology

Abstract

fetched live from OpenAlex

Recent studies in Bangladesh indicate that arsenic and manganese in tube-well water may increase the incidence of infant mortality. The study reported here examined whether these findings could be replicated. Data available from some 600 villages under the care of the nongovernmental organization (NGO) Gonoshasthaya Kendra included details of 29744 live births and 934 infant deaths in a 2-year period, with age and cause. These were analyzed by mean well levels of arsenic and manganese as reported by the British Geological Survey for the 12 upazillas. Odds ratios were calculated by age at death and cause. The effect of arsenic on all-cause infant mortality, although small and not significant, was consistent with earlier reports. The previous finding of an increased risk of infant mortality at concentrations of manganese > or =0.4 mg/L was not evident.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.012
GPT teacher head0.292
Teacher spread0.279 · 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.

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

Citations17
Published2010
Admission routes1
Has abstractyes

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