To CT or not to CT? The influence of computed tomography on the diagnosis of appendicitis in obese pediatric patients
Bibliographic record
Abstract
BACKGROUND: Appendicitis is a common pediatric query. However, obesity often results in nondiagnostic ultrasounds and increased likelihood of abdominal computed tomography (CT). Concern regarding radiation exposure led the Canadian Association of Radiologists to recommend foregoing CT when ultrasounds are nondiagnostic and clinical suspicion is high. We evaluated this recommendation by quantifying the influence of CT on the diagnosis of pediatric appendicitis. METHODS: We performed a 2-year retrospective case series of children presenting with suspected appendicitis. We stratified patients by weight (obese v. nonobese) and pediatric appendicitis score (PAS) and examined how often they received abdominal CT, why they received it, and its influence on diagnosis. RESULTS: Of 223 patients (84 obese, 139 nonobese), 54 received CT. Obese patients received CTs more frequently than nonobese patients (29% v. 22%). The most common reason for CT was a nondiagnostic ultrasound (75% in obese, 80% in nonobese patients). Sixty-five percent of CTs obtained after nondiagnostic ultrasounds confirmed the initial diagnosis, but the rates were 80% and 50%, respectively, when only obese and only nonobese patients were considered. Obese patients were 4 times more likely to have a CT confirming their initial appendicitis diagnosis. CONCLUSION: Because obese patients are more likely than nonobese patients to have a CT that confirms appendicitis, when treating an obese pediatric patient with suspected appendicitis and a nondiagnostic ultrasound, surgeons with a high clinical suspicion should strongly consider foregoing CT and proceeding with treatment.
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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 | 0.002 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".