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Record W2804406042 · doi:10.1093/pch/pxy054.099

SUBNORMAL BIRTHWEIGHT AND INDUSTRIAL AIR POLLUTANTS – A COMPARISON OF SPATIAL-TEMPORAL HOT SPOT PATTERNS

2018· article· en· W2804406042 on OpenAlexaboutno aff
Charlene C. Nielsen, Carl Amrhein, Jesús Serrano-Lomelin, Osmar R. Zai͏̈ane, Álvaro Osornio-Vargas

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileSmall for gestational agePollutantBirth weightCold spotHot spot (computer programming)GestationExposomeAir pollutantsPregnancyMedicineEnvironmental healthGeographyAir pollutionStatisticsChemistryBiologyPhysical geographyMathematics

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Disorders related to short gestation and low birth weight are the 2nd cause of infant death in Canada and have been increasing, especially in Alberta. Individual maternal risks are important but environmental exposures during pregnancy may restrict fetal growth. This contributes to small for gestational age (SGA: < tenth percentile weight for pregnancy duration) and low birth weight at term (LBWT: <2500 grams at ≥37 weeks-gestation). OBJECTIVES We examined the spatial-temporal patterns of SGA and LBWT with patterns of pollutants around conception, middle trimester, and birth. DESIGN/METHODS We aggregated postal code locations of mothers’ residences from the 2006–2012 birth registry in to space-time bins to analyze emerging hot spots. We applied the space-time pattern analysis on 70 industrial chemical emissions from the National Pollutant Release Inventory (NPRI) in estimated three month intervals. Then we statistically associated the classified patterns of SGA/LBWT with the pollutant patterns using the kappa statistic to determine how much the hot spot categories agree. The difference between kappa values indicated which trimester would be more important for which chemical. RESULTS ​There was an increasing trend for SGA (consecutive hot spots) and for LBWT (sporadic hot spots) in major urban centers. There was an increasing trend for 15 chemicals (varying hot spots). 28 chemical patterns had a kappa index greater than 0.2 with SGA or LBWT patterns. Although there is poor agreement between the space-time patterns, the maximum kappa values occurred mostly with LBWT and around birth. CONCLUSION Spatial-temporal patterns of chemicals identified in published literature (e.g. particulate matter and gases) agreed more with timing around conception; however, there were additional pollutants identified during the birth trimester. Our research is moving us toward a better understanding of the spatial-temporal link between environment and early health.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.044
GPT teacher head0.311
Teacher spread0.266 · 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 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
Published2018
Admission routes1
Has abstractyes

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