Social determinants of gastrointestinal malformation mortality in Brazil: a national study
Bibliographic record
Abstract
Introduction In Brazil, approximately 5% are born with a congenital disorder, potentially fatal without surgery. This study aims to evaluate the relationship between gastrointestinal congenital malformation (GICM) mortality, health indicators, and socioeconomic factors in Brazil. Methods GICM admissions (Q39–Q45) between 2012 and 2019 were collected using national databases. Patient demographics, socioeconomic factors, clinical management, outcomes, and the healthcare workforce density were also accounted for. Pediatric Surgical Workforce density and the number of neonatal intensive care units in a region were extracted from national datasets and combined to create a clinical index termed ‘NeoSurg’. Socioeconomic variables were combined to create a socioeconomic index termed ‘SocEcon’. Simple linear regression was used to investigate if the temporal changes of both indexes were significant. The correlation between mortality and the different indicators in Brazil was evaluated using Pearson’s correlation coefficient. Results Over 8 years, Brazil recorded 12804 GICM admissions. The Southeast led with 6147 cases, followed by the Northeast (2660), South (1727), North (1427), and Midwest (843). The North and Northeast reported the highest mortality, lowest NeoSurg, and SocEcon Index rates. Nevertheless, mortality rates declined across regions from 7.7% (2012) to 3.9% (2019), a 51.7% drop. The North and Midwest experienced the most substantial reductions, at 63% and 75%, respectively. Mortality significantly correlated with the indexes in nearly all regions (p<0.05). Conclusion Our study highlights the correlation between social determinants of health and GICM mortality in Brazil, using two novel indexes in the pediatric population. These findings provide an opportunity to rethink and discuss new indicators that could enhance our understanding of our country and could lead to the development of necessary solutions to tackle existing challenges in Brazil and globally.
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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 | 0.000 | 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".