Unveiling patterns in pediatric appendectomy: A comparative study on healthcare resource capacity and surgical decisions in Brazil
Bibliographic record
Abstract
BACKGROUND: Appendicitis is the most prevalent surgical emergency in children. This study examined hospital infrastructure, surgical techniques, patient demographics, and hospitalization parameters to assess the provision of safe and adequate care within the Brazilian public healthcare system. METHODS: Pediatric hospitalizations for acute appendicitis in 2022 were extracted from the Brazilian national database. We included all hospitalizations for patients aged 0-16 years with a primary ICD-10 diagnosis of acute appendicitis who underwent an operation. Parameters of interest were the type of surgical approach, mortality, and total cost of hospitalization. Facilities were defined as basic-facility, full-facility, and pediatric according to the level of pediatric resources available. RESULTS: In 2022, there were 29,983 pediatric appendectomies due to acute appendicitis. Of these, 90.2% were open appendectomies. Most occurred in basic-facility general hospitals (53.0%), followed by full-facility (35.2%) and pediatric hospitals (11.8%). Full-facility hospitals had a higher median cost (USD126.3, IQR 99.5-154.4) compared to basic (USD96.8, IQR 87.6-130.1) and pediatric hospitals (USD103.0, IQR 91.9-117.5), though the cost difference between basic and pediatric was not significant (p = 0.367). Death was a rare event across all levels of hospital infrastructure and for all types of procedures performed. CONCLUSIONS: The majority of hospitalizations for acute appendicitis occurred in hospitals with minimal pediatric infrastructure. Open appendectomies remain the most predominant procedure across all hospital types.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".