Preoperative metabolic acidosis in infants with gastroschisis
Bibliographic record
Abstract
INTRODUCTION: There is little in literature regarding preoperative management of infants with gastroschisis. It is unclear if these infants develop metabolic acidosis as a consequence of prolonged intrauterine gut compromise or dehydration secondary to increased fluid loss. AIM: To assess the frequency of preoperative metabolic acidosis in infants with gastroschisis and investigate whether this acidosis reflects degree of gut compromise. METHODS: All infants with gastroschisis born between May 2005 and April 2013 in a single tertiary care center were reviewed. Metabolic acidosis was defined by the presence of pH <7.26 and serum bicarbonate <18.5 or base excess < -8.5 mmol/l. Infants with significant birth depression were excluded. Maternal and neonatal data were collected. Frequency of preoperative metabolic acidosis and its association with gastroschisis prognostic score (GPS), time to first and time to reach full feeds were investigated. RESULTS: Sixty infants were identified, 11 were excluded (birth depression/lack of preoperative blood gases). Median preoperative total fluid intake was 130 ml/kg/d. Nine infants (18%) had metabolic acidosis at a median age of 1.2 hours. No association was found between metabolic acidosis or serum lactate and GPS, age at first feed or age at full feeds. CONCLUSION: Preoperative metabolic acidosis was identified in a significant number of patients with gastroschisis despite high fluid intake. It does not appear to be associated with the degree of gut compromise. Using metabolic acidosis as an indication of dehydration in these patients needs more investigation.
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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 | 0.003 |
| 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".