Hypotension and Brain Injury in Asphyxiated Newborns Treated with Hypothermia
Bibliographic record
Abstract
Objective This study aimed to assess the incidence of hypotension in asphyxiated newborns treated with hypothermia, the variability in treatments for hypotension, and the impact of hypotension on the pattern of brain injury. Study Design We conducted a retrospective cohort study of asphyxiated newborns treated with hypothermia. Mean blood pressures, lactate levels, and inotropic support medications were recorded during the hospitalization. Presence and severity of brain injury were scored using the brain magnetic resonance imaging (MRI) obtained after the hypothermia treatment was completed. Results One hundred and ninety term asphyxiated newborns were treated with hypothermia. Eighty-one percent developed hypotension. Fifty-five percent of the newborns in the hypotensive group developed brain injury compared with 35% of the newborns in the normotensive group (p = 0.04). Twenty-nine percent of the newborns in the hypotensive group developed severe brain injury, compared with only 15% in the normotensive group. Nineteen percent of the newborns presenting with volume- and/or catecholamine-resistant hypotension had near-total injury, compared with 6% in the normotensive group and 8% in the group responding to volume and/or catecholamines. Conclusion Hypotension was common in asphyxiated newborns treated with hypothermia and was associated with an increased risk of (severe) brain injury in these newborns.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".