Diagnostic Performance of a Staged Pathway for Imaging Acute Appendicitis in Children
Bibliographic record
Abstract
INTRODUCTION: The objective of this work is to assess the performance of our staged diagnostic pathway in the evaluation of suspected appendicitis cases in children. The pathway consisted of clinical assessment by the emergency physician, performing initial ultrasound (US), consultation, and clinical reevaluation by the surgery team followed by a repeat focused US scan in inconclusive cases. Computed tomography (CT) was limited to cases where the repeat US remained inconclusive and the clinical reassessment indicated persistent concerns for appendicitis. METHOD: Retrospective review of the electronic medical records of 206 consecutive children who presented to our emergency department with acute abdominal pain and underwent US examination for suspected appendicitis. The imaging findings, management plan, and surgical outcome (in those who underwent surgery) were reviewed. The diagnostic performance of the initial US, repeat US, and the full imaging protocol were evaluated including the negative appendectomy rate (NAR) and the number of CT scans performed. RESULTS: Of the 206 cases, 73 (35.4%) had appendicitis. Computed tomography was performed in 9 (4.3%) of 206 cases. The US/CT ratio was 23:1. Our approach showed a diagnostic accuracy of 95.6% (197/206), sensitivity of 97.3% (73/75), specificity of 93.7% (124/133), positive predictive value of 89.0% (73/82), and negative predictive value of 98.7% (82/95). The NAR was 2.7% (2/72). The accuracy of the protocol is higher than that of the initial US alone (61.2%; 126/206) and that of the repeat US (84.2%; 16/19). CONCLUSION: The strategy of repeating limited focused US followed by CT scan in cases that remain inconclusive has good diagnostic accuracy and reasonable NAR and decreases the number of CT scans.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".