Paucity of renal follow-up by school age after neonatal cardiac surgery
Bibliographic record
Abstract
INTRODUCTION: There are little data about renal follow-up of neonates after cardiovascular surgery and no guidelines for long-term renal follow-up. Our objectives were to assess renal function follow-up practice after neonatal cardiac surgery, evaluate factors that predict follow-up serum creatinine measurements including acute kidney injury following surgery, and evaluate the estimated glomerular filtration rate during follow-up using routinely collected laboratory values. METHODS: Two-centre retrospective cohort study of children 5-7 years of age with a history of neonatal cardiac surgery. Univariable and multivariable analyses were performed to determine factors associated with post-discharge creatinine measurements. Glomerular filtration rate was estimated for each creatinine using a height-independent equation. RESULTS: Seventeen of 55 children (30%) did not have any creatinine measured following discharge after surgery until the end of study follow-up, which occurred at a median time of 6 years after discharge. Of the 38 children who had the kidney function checked, 15 (40%) had all of their creatinine drawn only in the context of a hospitalisation or emergency department visit. Acute kidney injury following surgery did not predict the presence of follow-up creatinine measurements. CONCLUSIONS: A large proportion of neonates undergoing congenital heart repair did not have a follow-up creatinine measured in the first years following surgery. In those that did have a creatinine measured, there did not appear to be any identified pattern of follow-up. A follow-up system for children who are discharged from cardiac surgery is needed to identify children with or at risk of chronic kidney disease.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".