Condiciones socioeconómicas más bajas se asocian con tasas de sepsis infantil más altas pero con resultados similares
Bibliographic record
Abstract
Sepsis is an important cause of pediatric morbidity and mortality, especially in low-income countries. Data on regional prevalence, mortality trends, and their relationship with socioeconomic variables are scarce. OBJECTIVE: to determine the regional prevalence, mortality, and sociodemographic situation of patients diagnosed with severe sepsis (SS) and septic shock (SSh) admitted to Pediatric Intensive Care Units (PICUs). PATIENTS AND METHOD: patients aged 1 to 216 months admitted to 47 participating PICUs with a diagnosis of SS or SSh between January 1, 2010, and December 31, 2018, were included. Secondary analysis was performed on the Argentine Society of Intensive Care Benchmarking Quality Program (SATI-Q) database for SS and SSh and a review of the annual reports of the Argentine Ministry of Health and the National Institute of Statistics and Census for the sociodemographic indices of the respective years. RESULTS: 45,480 admissions were recorded in 47 PICUs, 3,777 of them with a diagnosis of SS and SSh. The combined prevalence of SS and SSh decreased from 9.9% in 2010 to 6.6% in 2018. The combined mortality decreased from 34.5% to 23.5%. Multivariate analysis showed that the Odds ratio (OR) of the association between SS and SSh mortality was 1.88 (95% CI: 1.46-2.32) and 2.4 (95% CI: 2.16-2.66), respectively, adjusted for malignant disease, PIM2, and mechanical ventilation. The prevalence of SS and SSh in different health regions (HR) was associated with the percentage of poverty and infant mortality rate (p < 0.001). However, there was no association between sepsis mortality and HR adjusted for PIM2. CONCLUSIONS: Prevalence and mortality of SS and SSh have decreased over time in the participating PICUs. Lower socioeconomic conditions were associated with higher prevalence but similar sepsis outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".