Geographical Analysis of the Distribution of Publications Describing Spatial Associations among Outdoor Environmental Variables and Really Small Newborns in the USA and Canada
Bibliographic record
Abstract
Newborns defined as being of “low birth weight” (LBW) or “small for gestational age” (SGA) are global health issues of concern because they are vulnerable to mortality and morbidity. Prenatal exposures may contribute to LBW/SGA. In this review, we searched peer-reviewed scientific literature to determine what location-based hazards have been linked with LBW/SGA in the industrialized nations of Canada and the USA. After selecting studies based on inclusion/exclusion criteria, we entered relevant details in to an evidence table. We classified and summarized 159 articles based on type of environment (built = 108, natural = 10, and social = 41) and general category of environmental variables studied (e.g., air pollution, chemical, water contamination, waste site, agriculture, vegetation, race, SES, etc.). We linked the geographic study areas by province/state to political boundaries in a GIS to map the distributions and frequencies of the studies. We compared them to maps of LBW percentages and ubiquitous environmental hazards, including land use, industrial activity and air pollution. More studies had been completed in USA states than Canadian provinces, but the number has been increasing in both countries from 1992 to 2018. Our geographic inquiry demonstrated a novel, spatially-focused review framework to promote understanding of the human ‘habitat’ of shared environmental exposures that have been associated with LBW/SGA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.038 | 0.064 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".