Metabolic acidosis rather than hypo/hypercapnia in the first 72 hours of life associated with intraventricular hemorrhage in preterm neonates
Bibliographic record
Abstract
Aim Safe limits of arterial partial pressure of carbon dioxide (PaCO2) and acidosis in premature infants are not well defined. Both respiratory and systemic illness along with center-specific ventilation strategies contribute to PaCO2 fluctuations and acid–base imbalances during the critical time period of first 72 h of life. This study evaluated the association between early blood gas parameters and intraventricular hemorrhage (IVH) in preterm infants.Methods This retrospective observational study included neonates with a gestational age (GA) of ≤29 wks, who had at least 7 blood gas analysis done within the first 72 h of life. By adjusting for known variables that predispose to IVH, multivariable logistic regression analysis was used to study the association of PaCO2 and acid–base measures with the risk of IVH.Results Between 2013–2016, among 272 neonates who met inclusion criteria and were assessed for IVH on cranial ultrasound within first week of life, 101 neonates [mean GA of 25 ± 1.5 wks] had IVH and 171 neonates [mean GA of 25 ± 1.6 wks] had normal scans. After adjustment for confounding variables, higher values of maximum lactate (OR = 1.18, 95% CI = 1.1–1.3, p < .0001) and maximum base deficit (OR = 1.19, 95% CI = 1.1–1.2, p < .0001) within 72 h of life increased the likelihood of any grade of IVH. However, time-weighted average PaCO2, maximum and minimum PaCO2 had no statistically significant effect on the risk of IVH. The relationship remained unchanged even when moderate–severe IVH was considered as the primary outcome.Conclusion Severe metabolic acidosis rather than hypo/hypercapnia during the first 72 h of life was associated with higher odds of IVH in infants born at ≤29 wks of gestation. Future studies determining levels of PaCO2 that is safe for premature brain would need to control for the metabolic component of acidosis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".