Postoperative complications after gastrointestinal pediatric surgical procedures: outcomes and socio-demographic risk factors
Bibliographic record
Abstract
BACKGROUND: Several socio-demographic characteristics are associated with complications following certain pediatric surgical procedures. In this comprehensive study, we sought to determine socio-demographic risk factors and resource utilization of children with complications after common pediatric surgical procedures. METHODS: We performed a population-based cohort study utilizing the 2016 Healthcare Cost and Use Project Kids' Inpatient Database (KID) to identify and characterize pediatric patients (age 0-21 years) in the United States with common inpatient pediatric gastrointestinal surgical procedures: appendectomy, cholecystectomy, colonic resection, pyloromyotomy and small bowel resection. Multivariable logistic regression modeling was used to identify socio-demographic predictors of postoperative complications. Length of stay and hospitalization costs for patients with and without postoperative complications were compared. RESULTS: A total of 66,157 pediatric surgical hospitalizations were identified. Of these patients, 2,009 had postoperative complications. Male sex, young age, African American and Native American race and treatment in a rural hospital were associated with significantly greater odds of postoperative complications. Mean length of stay was 4.58 days greater and mean total costs were $11,151 (US dollars) higher in the complication cohort compared with patients without complications. CONCLUSIONS: Postoperative complications following inpatient pediatric gastrointestinal surgery were linked to elevated healthcare-related expenditure. The identified socio-demographic risk factors should be considered in the risk stratification before pediatric surgical procedures. Targeted interventions are required to reduce preventable complications and surgical disparities.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".