Incidence and impact of malnutrition in patients with Fontan physiology
Bibliographic record
Abstract
BACKGROUND: Single-ventricle patients require a series of surgeries, with the final stage being the Fontan. This form of circulation results in several long-term complications, but the impact and consequences of nutrition status remain unclear. We sought to evaluate the incidence of malnutrition in Fontan patients and the impact on outcomes. METHODS: This study was a retrospective cohort study of children who underwent Fontan surgery between 1997 and 2018. Clinical, demographic, and nutrition data were collected, including weight, height, body mass index (BMI), and their respective z scores (z score for weight-for-age [WAZ], z score for height-for-age [HAZ], and z score for BMI-for-age [BMIZ]) pre-Fontan, at discharge, 6 months, and 1, 5, and 10 years post-Fontan. Malnutrition status was categorized using the American Society for Parenteral and Enteral Nutrition guidelines and the Michigan MTool. Fontan failure was defined as listing for heart transplant or death. RESULTS: Of the 69 patients, moderate-severe malnutrition occurred at any time point in 11% (n = 8) by WAZ, 16% (n = 11) by HAZ, and 6% (n = 4) by BMIZ. Moderate-severe malnutrition persisted in 6.5%-12.9% at 10 years post-Fontan. Compared with the pre-Fontan period, there was no change in these parameters over time. There was no statistically significant difference in Fontan failure between degrees of pre-Fontan malnutrition. CONCLUSION: There is a 6%-16% incidence of moderate-severe malnutrition in Fontan patients. Malnutrition is a condition that remains present in follow-up. There was no association with anthropometric parameters and transplant-free survival. A prospective multi-institutional study is needed to understand the impact of malnutrition on long-term outcomes.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".