LBW and IUGR temporal trend in 4 population-based birth cohorts: the role of economic inequality
Bibliographic record
Abstract
BACKGROUND: Low/medium income countries, with health inequalities present high rates of neonates having low birthweight and/or are small for the gestational age. This study aims to analyze the absolute and relative income inequality in the occurrence of low birthweight and small size for gestational age among neonates in four birth cohorts from southern Brazil in 1982, 1993, 2004, and 2011. METHODS: The main exhibit was monthly family income. The outcomes were birth with low birthweight or small for the gestational age. The inequalities were calculated using the Slope Index of Inequality and the Relative Index of Inequality adjusted for maternal skin color, schooling, age, and marital status. RESULTS: In all birth cohorts, poorer mothers were at greater odds of having neonates with low birthweight or small for the gestational age. There was a tendency to decrease the prevalence of small for gestational age in poorer families associated with the reduction of inequalities over the past decades, which was not observed regarding low birthweight. CONCLUSIONS: Economic inequalities occurred in neonates with low birthweight and with intrauterine growth restriction in the four studies, with a higher incidence of inadequate neonatal outcomes in the poorer families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".