Nutrition Support of the Postoperative Cardiac Surgery Child
Bibliographic record
Abstract
There may be a correlation in critically ill children between the accuracy of estimated energy requirement and infection, mortality, and length of stay. Historically, energy needs were estimated using predictive equations with stress factor adjustments. The purpose of this review is to evaluate the evidence for indirect calorimetry, predictive equations, and other clinical indicators (ie, patient outcomes) to estimate energy requirements of the postoperative, critically ill, cardiac infant. Consistent with current guidelines, indirect calorimetry provides the best estimate of energy requirements for critically ill children. Predictive equations are unreliable, either over- or underestimate energy requirements, and do not take into account the metabolic changes that occur in the postoperative cardiac infant. To address the changing metabolic state throughout the course of illness, clinicians need to individualize recommendations by implementing frequent indirect calorimetry measurements at bedside. Actual energy delivery to the postoperative cardiac surgery child in the pediatric intensive care unit (PICU) can be further hindered by many procedural and patient barriers. The provision of appropriate caloric requirements may help clinicians correct the metabolic state and promote recovery and anabolism. Therefore, optimizing nutrition intake of the postoperative, cardiac surgical child requires a paradigm shift toward individualized nutrition prescription, in the context of a PICU-specific feeding algorithm.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".