Pediatric appendicitis in “real-time”: The value of sonography in diagnosis and treatment
Bibliographic record
Abstract
OBJECTIVES: To determine the accuracy of sonography in the diagnosis of clinically equivocal appendicitis, and to identify the factors leading to an inaccurate ultrasound diagnosis. The impact of sonographic findings on clinical management and outcome of children with appendicitis is examined. METHODS: We performed a retrospective review of 317 children who attended the emergency department (ED) of a children's hospital for acute abdominal pain for which acute appendicitis was the main differential diagnosis. They had ultrasound because the diagnosis was uncertain clinically. RESULTS: The positive predictive value of ultrasound for appendicitis was 0.92, and the negative predictive value was 0.88. The sensitivity and specificity could not be determined because there were 43 patients with equivocal ultrasound results. The pitfalls hindering the accuracy of ultrasound diagnosis included a high incidence of perforated appendicitis at presentation, the retrocecal appendix, and other technical factors such as abdominal guarding, excessive bowel gas, obesity, inadequate bladder filling, and the uncooperative patient. When ultrasound findings were combined with clinical judgment in clinical management, there were only five cases of non-therapeutic laparotomy and eight cases of delayed surgery due to missed diagnosis in our cohort. CONCLUSIONS: Ultrasound is a useful for the evaluation of acute abdominal pain in children. However, in the setting of a pediatric hospital ED, the accuracy of ultrasound and its ability to improve early hospital triage may be reduced. Repeated clinical review is still essential and in selected cases, appendiceal CT scan may be required to guide therapeutic decision making.
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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 | 0.004 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".