Acute Appendicitis in Young Children: Cost-effectiveness of US versus CT in Diagnosis—A Markov Decision Analytic Model
Bibliographic record
Abstract
PURPOSE: To compare the cost-effectiveness of different imaging strategies in the diagnosis of pediatric appendicitis by using a decision analytic model. MATERIALS AND METHODS: Approval for this retrospective study based on literature review was not required by the institutional Research Ethics Board. A Markov decision model was constructed by using costs, utilities, and probabilities from the literature. The risk of radiation-induced cancer was modeled by using the Biological Effects of Ionizing Radiation VII report, which is based primarily on data from atomic bomb survivors. The three imaging strategies were ultrasonography (US), computed tomography (CT), and US followed by CT if the initial US study was negative. The model simulated the short-term and long-term outcomes of the patients, calculating the average quality-adjusted life span and health care costs. RESULTS: For a single abdominal CT study in a 5-year-old child, the lifetime risk of radiation-induced cancer would be 26.1 per 100,000 in female and 20.4 per 100,000 in male patients. In the base-case analysis, US followed by CT was the most costly and most effective strategy, CT was the second-most costly and second-most effective strategy, and US was the least costly and least effective strategy. The incremental cost-effectiveness ratios (ICERs) of CT to US and of US followed by CT to US were both well below the societal willingness-to-pay threshold of $50,000 (in U.S. dollars). The ICER of US followed by CT to CT was less than $10,000 in both male and female patients. CONCLUSION: In a Markov-based decision model of pediatric appendicitis, the most cost-effective method of imaging pediatric appendicitis was to start with a US study and follow each negative US study with a CT examination.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".