A Prospective, Cohort Study of the Effect of Acute and Chronic Malnutrition on Length of Stay in Children Having Surgery in Rwanda
Bibliographic record
Abstract
BACKGROUND: Malnutrition is common in pediatric surgical patients, but there are little data from low-income countries that estimate the association of malnutrition with surgical outcomes. We aimed to determine the prevalence of malnutrition and its association with length of stay (LOS) among pediatric surgical patients in Kigali, Rwanda. METHODS: We conducted a prospective observational cohort study. We enrolled surgical patients between 1 month and 15 years of age. We measured the association of acute malnutrition (wasting) and chronic malnutrition (stunting) with postoperative LOS using log-gamma regression to account for the skewed LOS distribution. Adjustment was made for sex, age, elective versus emergency surgery, household income, and American Society of Anesthesiologists (ASA) classification. RESULTS: Of 593 children, 124 children (21.2%) had acute malnutrition (wasting) with 39 (6.6%) severely wasted. A total of 160 (26.9%) children had chronic malnutrition (stunting), with 81 (13.7%) severely stunted. Median (interquartile range [IQR]) LOS after surgery was 2 (1-5) days for children with mild/no wasting, 6 (2.5-12.5) days for children with moderate wasting, and 6 (2-15) days with severe wasting. Median (IQR) LOS after surgery was 2 (1-6) days for children with mild/no stunting, 3 (1-3) days for children with moderate stunting, and 5 (2.3-11.8) days with severe stunting malnutrition. After adjustment for confounders, the moderate wasting was associated with increased LOS, with ratio of means (RoM), 1.6; 95% confidence interval [CI], 1.3-2.0; P < .0001. Severe wasting was not associated with increased LOS (RoM, 1.3; 95% CI, 0.9-1.7; P = .12). Severe, but not moderate, stunting was associated with increased LOS (RoM, 1.9; 1.5-2.4; P < .0001). CONCLUSIONS: Malnutrition is prevalent in >20% of children presenting for surgery and associated with increased LOS after surgery, even after accounting for individual and family-level confounders. Although some aspects of malnutrition may relate to the surgical condition, severe malnutrition may represent a modifiable social risk factor that could be targeted to improve postoperative outcomes and resource use. Severely stunted children should be identified as at risk of having delayed recovery after surgery.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".