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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".