Infection and white matter injury in infants with congenital cardiac disease
Bibliographic record
Abstract
More than 60% of newborns with severe congenital cardiac disease develop perioperative brain injuries. Known risk factors include: pre-operative hypoxemia, cardiopulmonary bypass characteristics, and post-operative hypotension. Infection is an established risk factor for white matter injury in premature newborns. In this study, we examined term infants with congenital cardiac disease requiring surgical repair to determine whether infection is associated with white matter injury. Acquired infection was specified by site - bloodstream, pneumonia, or surgical site infection - according to strict definitions. Infection was present in 23 of 127 infants. Pre- and post-operative imaging was evaluated for acquired injury by a paediatric neuroradiologist. Overall, there was no difference in newly acquired post-operative white matter injury in infants with infection (30%), compared to those without (31%). When stratified by anatomy, infants with transposition of the great arteries, and bloodstream infection had an estimated doubling of risk of white matter injury that was not significant, whereas those with single ventricle anatomy had no apparent added risk. When considering only infants without stroke, the estimated association was higher, and became significant after adjusting for duration of inotrope therapy. In this study, nosocomial infection was not associated with white matter injury. Nonetheless, when controlling for risk factors, there was an association between bloodstream infection and white matter injury in selected sub-populations. Infection prevention may have the potential to mitigate long-term neurologic impairment as a consequence of white matter injury, which underscores the importance of attention to infection control for these patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".