Incidence of low birth weight in Mexico: A descriptive retrospective study from 2008–2017
Bibliographic record
Abstract
According to the WHO, low birth weight (LBW) affects 15-20% of newborns worldwide. In Mexico, there are no national, state, nor municipal estimates that inform the country's situation over time. The purpose of this study was to estimate the incidence of LBW at the national, state, and municipal levels from 2008 to 2017, and to estimate the LBW incidence based on maternal sociodemographic characteristics, prenatal care and marginalization indexes at the national level using open national data. We used spatial data analysis to georeferenced LBW incidence at the three levels of geographical disaggregation studied. At the national level, the incidence of LBW increased progressively from 6.2% (2008) to 7.1% (2017), and the country's capital represented the area with the highest incidence. Southeastern and central states reported the highest LBW regional incidence. At the municipal level, the number of municipalities with an incidence of LBW ≥8% increased in both male and female newborns. The incidence of LBW was higher as the marginalization indexes increases. The results from this study may assist in the identification of vulnerable groups and the development of public health programs and policies with an intersectoral approach that improves maternal and child nutrition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".