Therapeutic hypothermia success for hypoxic‐ischaemic encephalopathy in Latin America: Eight‐year experience in <scp>EpicLatino</scp> Neonatal Network
Bibliographic record
Abstract
AIM: A study reported that therapeutic hypothermia (TH) did not reduce the combined prognosis of mortality and disability at 18 months, in low- and middle-income countries for patients with hypoxic ischaemic encephalopathy (HIE) who received TH, suggesting its no implementation in these regions. We described characteristics, mortality, and neurological response before and after the use of TH in newborns with HIE within the EpicLatino Neonatal Network (ENN) and described the population of infants with HIE treated and not treated with TH. METHODS: Data were collected from 2015 to 2022 for patients with HIE. Mortality rates and Sarnat scores were compared before and after TH. The Wilcoxon Signed-Rank Test was used for comparisons. RESULTS: In this observational study 518 neonates of our total population of 26 970, had HIE (1.92%) of whom 150 underwent TH. Ten out of 21 neonatal intensive care units (NICUs) provided TH. The Wilcoxon Signed Rank Test for 138 cases with complete data showed a significant difference. CONCLUSION: The findings support the benefits of TH in HIE within this cohort. TH should not be withheld solely due to the economic status of the country. A strict patient selection and TH protocol are essential.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".