Fluid overload in newborns undergoing abdominal surgery: a retrospective study
Bibliographic record
Abstract
Background Fluid management in newborns undergoing surgery can be challenging due to difficulties in accurately assessing volume status in context of high fluid needs perioperatively and postoperative third-space fluid loss. Fluid overload can be associated with an increase in neonatal morbidity and mortality.Objective Our objective was to determine the burden of fluid overload and to evaluate their associations with adverse effects among infants undergoing abdominal surgery at a tertiary perinatal center.Methods Patients from our Neonatal Intensive Care Unit who underwent abdominal surgery from January 2017 to June 2019 were included in this retrospective cohort study. Fluid balance was assessed based on the maximum percentage change in body weight at 3- and 7-postoperative days.Results Sixty infants were included, with a median [interquartile range] gestational age (GA) of 29 [25–36] weeks and birth weight of 1240 [721–2871] grams. The median daily actual fluid intake was significantly higher than the prescribed fluid intake in the first 7 postoperative days (163 vs. 145 mL/kg, p < .01). The median maximum change of body weight by postoperative days 3 and 7 were 6% [3–13] and 11% [5–17], respectively. A 1% increase in weight within the first 3 postoperative days was associated with a 0.6-day increase for invasive ventilatory support (p = .012). The correlation was still significant after adjusting for GA (p = .033).Conclusion Fluid overload within the first 3 postoperative days was associated with an increase in ventilator support among infants. Careful attention to fluid management may affect the optimization of outcomes for newborns undergoing abdominal surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".