Variability in Assisted Ventilation in Neonatal Intensive Care Units in Latin America. Influence of High Altitude
Bibliographic record
Abstract
Objective: To examine the variability in the duration and use of assisted ventilation among Neonatal Intensive Care Units (NICUs) in Latin America, with particular focus on the influence of high-altitude settings. Study Design: This multicenter observational study analyzed data from the EpicLatino database (2015–2022), encompassing 32 NICUs across Latin America and the Caribbean. The study population included preterm infants born at ≤32 weeks of gestation. Assisted ventilation was defined as the use of invasive mechanical ventilation, high-frequency oscillatory ventilation, continuous positive airway pressure, or high-flow nasal cannula. A competing risks regression model was applied to assess the association between total ventilation duration and clinical, altitude, and temporal factors, accounting for length of stay and in-hospital mortality. Results: Of the total cohort, approximately 40% of infants were managed in NICUs located at altitudes above 2,000 meters. Use of assisted ventilation increased significantly in the post-pandemic period (2020–2022) with a subhazard ratio (SHR) of 1.2 (95% CI: 1.1–1.3). High-altitude units showed significant variability in ventilation duration compared to the reference unit; however, no consistent pattern distinguished high-altitude from sea-level units. Altitude could not be analyzed independently due to collinearity with unit of origin. Conclusions: Significant inter-unit variability in the duration of assisted ventilation was observed among Latin American NICUs, with no uniform trend attributable to high altitude. These findings underscore the importance of unit-specific practices and highlight the potential for standardizing ventilatory strategies through quality improvement initiatives, particularly in high-altitude settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".