Post-discharge complications and hospital readmissions are associated with nutritional risk and malnutrition status in a cohort of Canadian pediatric patients
Bibliographic record
Abstract
BACKGROUND: This study constitutes a secondary analysis of a prospective cohort aiming to evaluate the potential correlation between nutritional risk and status at admission with the occurrence of post-discharge complications and hospital readmissions in children receiving care at high resource Centres. METHODS: Data was collected from 5 Canadian tertiary pediatric Centers between 2012 and 2016. Nutritional risk and status were evaluated at hospital admission with validated tools (STRONGkids and Subjective Global Nutrition Assessment [SGNA]) and anthropometric measurements. Thirty days after discharge, occurrence of post-discharge complications and hospital readmission were documented. RESULTS: A total of 360 participants were included in the study (median age, 6.1 years; median length of stay, 5 days). Following discharge, 24.1% experienced complications and 19.5% were readmitted to the hospital. The odds of experiencing complications were nearly tripled for participants with a high nutritional risk compared to a low risk (OR = 2.85; 95% CI [1.08-7.54]; P = 0.035) and those whose caregivers reported having a poor compared to a good appetite (OR = 2.96; 95% CI [1.59-5.50]; P < 0.001). According to SGNA, patients identified as malnourished had significantly higher odds of complications (OR, 1.92; 95% CI, 1.15-3.20; P = 0.013) and hospital readmission (OR, 1.95; 95% CI, 1.12-3.39; P = 0.017) than to those well-nourished. CONCLUSIONS: This study showed that complications and readmission post-discharge are common, and these are more likely to occur in malnourished children compared to their well-nourished counterparts. Enhancing nutritional care during admission, at discharge and in the community may be an area for future outcome optimization.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".