Utility and Diagnostic Accuracy of Sonography in Detecting Appendicitis in a Community Hospital
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the utility and accuracy of sonography in diagnosing acute appendicitis in patients with suspected acute appendicitis in a general community hospital. MATERIALS AND METHODS: All reports relating to appendicitis were retrospectively obtained from archived transcription reports of nine radiologists from a geographically constrained hospital between December 1999 and December 2003 by a search on the keyword "appendicitis." These files were correlated with the histopathology reports from surgical appendectomy or findings from clinical follow-up during the same period. A survey eliciting the views of five local surgeons on the utility of sonography for the detection of acute appendicitis was also collected. RESULTS: Sonography reports for 667 patients (mean age, 34 years; range, 6-93 years) were obtained. Of these, a total of 174 had pathologically proven appendicitis and 145 had positive findings for appendicitis on sonography. The accuracy was 92%; sensitivity, 83%; and specificity, 95%. The positive predictive value was 86%, and the negative predictive value was 94%. Three of the five surveyed surgeons indicated they used sonography less than 25% of the time, with none using it more than 75%. CONCLUSION: The sensitivity, specificity, accuracy, and positive and negative predicative values of sonography performed by general radiologists in a community hospital are comparable to statistics quoted in the literature for academic institutions. The most common error was the tendency to misclassify appendixes under 6 mm. Most surgeons surveyed stated their use of sonography would increase if sonography yielded a sensitivity and specificity of 85% or greater.
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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 | 0.032 |
| 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 | 0.001 |
| 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".