Poor postnatal weight growth is a late finding after sepsis in very preterm infants
Bibliographic record
Abstract
OBJECTIVE: To characterise the association between sepsis and postnatal weight growth when accounting for the degree of growth restriction present at birth. DESIGN: Retrospective matched cohort study using data from the Postnatal Growth and Retinopathy of Prematurity study. Participants were born with birth weights of <1500 g or gestational ages of <32 weeks between 2006 and 2011 at 29 neonatal centres in the USA and Canada. Sepsis was defined as a culture-confirmed bacterial or fungal infection of the blood or cerebrospinal fluid before 36 weeks' postmenstrual age (PMA). Growth was assessed as the change in weight z-score between birth and 36 weeks' PMA. RESULTS: Of 4785 eligible infants, 813 (17%) developed sepsis and 693 (85%) were matched 1:1 to controls. Sepsis was associated with a greater decline in weight z-score (mean difference -0.09, 95% CI -0.14 to -0.03). Postnatal weight growth failure (decline in weight z- score>1) was present in 237 (34%) infants with sepsis and 179 (26%) controls (adjusted OR 1.49, 95% CI 1.12 to 1.97). Longitudinal growth trajectories showed similar initial changes in weight z-scores between infants with and without sepsis. By 3 weeks after sepsis onset, there was a greater decline in weight z-scores relative to birth values in those with sepsis than without sepsis (delta z-score -0.89 vs -0.77; mean difference -0.12, 95% CI -0.18 to -0.05). This significant difference persisted until 36 weeks or discharge. CONCLUSION: Infants with sepsis had similar early weight growth trajectories as infants without sepsis but developed significant deficits in weight that were not apparent until several weeks after the onset of sepsis.
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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 | 0.005 |
| 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 | 0.001 | 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".