381In-hospital mortality for infants with HIE who received therapeutic hypothermia: a propensity score-matched cohort study
Bibliographic record
Abstract
Abstract Background In 2016, the Academic Medical Center Neonatal Encephalopathy Task Force recommended therapeutic hypothermia (TH) as the standard-of-care for hypoxic ischemic encephalopathy (HIE). However, not all infants who meet the criteria for TH receive this treatment. The purpose of this study was to compare the risk of mortality for infants with HIE who did and did not receive TH, after accounting for confounders associated with receipt of TH. Methods A retrospective cohort study was conducted using the 2016 National Inpatient Sample (NIS), which contains 20% of all hospital discharges in the United States. Infants were included if they were diagnosed with HIE and were eligible for TH. Nearest-neighbor propensity score-matching (1:1) without replacement was performed prior to logistic regression analysis. The average treatment effect of TH was calculated to estimate the odds of mortality. Results There were 211 infants with HIE who received TH, which is an estimated proportion of 24.8% (95% CI: 20.9-29.1%). Infants who received TH were more likely to have a seizure (p < 0.05), be transferred from another hospital (p < 0.001), and have the highest Risk of Mortality scores (p < 0.05). The odds of mortality were 0.91 (95% CI: 0.85-0.97) for infants that received TH, compared to those who did not. Conclusions Receipt of TH varied across patient groups and was associated with clinical risk factors. The odds of in-hospital mortality were lower in infants who received TH. Key messages Infants who received TH had a decreased risk of in-hospital mortality compared to infants who did not receive TH.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".