Major Surgery, Brain Injury, and Neurodevelopmental Outcomes in Very Preterm Infants
Bibliographic record
Abstract
Objectives: We determined whether (1) major surgery is associated with an increased risk for brain injury and adverse neurodevelopment and (2) brain injury modifies associations between major surgery and neurodevelopment in very preterm infants. Methods: Prospectively enrolled infants across three tertiary NICUs underwent early-life and/or term-equivalent-age MRI to detect moderate-severe brain injury. 18-month neurodevelopmental outcomes were assessed with Bayley Scales of Infant and Toddler Development, 3rd edition. Multivariable logistic and linear regressions were used to determine associations of major surgery with brain injury and neurodevelopment, adjusting for clinical confounders. Results: There were 294 infants in this study. Major surgery was associated with brain injury (OR 2.54, 95% CI [1.12, 5.75], p=0.03) and poorer motor outcomes (β=-7.92, 95% CI [-12.21, -3.64], p<0.001), adjusting for clinical confounders. Brain injury x major surgery interaction significantly predicted motor scores (p=0.04): lowest motor scores were in infants who required major surgery and had brain injury. Discussion: There is an increased risk for brain injury and adverse motor outcomes in very preterm infants who require major surgery, which may be a marker of clinical illness severity. Routine brain MRI to detect brain injury and close neurodevelopmental surveillance should be considered in this subgroup of infants.
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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".