Neutral<i>vs</i>positive oral contrast in diagnosing acute appendicitis with contrast-enhanced CT: sensitivity, specificity, reader confidence and interpretation time
Bibliographic record
Abstract
OBJECTIVE: The study compared the sensitivity, specificity, confidence and interpretation time of readers of differing experience in diagnosing acute appendicitis with contrast-enhanced CT using neutral vs positive oral contrast agents. METHODS: Contrast-enhanced CT for right lower quadrant or right flank pain was performed in 200 patients with neutral and 200 with positive oral contrast including 199 with proven acute appendicitis and 201 with other diagnoses. Test set disease prevalence was 50%. Two experienced gastrointestinal radiologists, one fellow and two first-year residents blindly assessed all studies for appendicitis (2000 readings) and assigned confidence scores (1=poor to 4=excellent). Receiver operating characteristic (ROC) curves were generated. Total interpretation time was recorded. Each reader's interpretation with the two agents was compared using standard statistical methods. RESULTS: Average reader sensitivity was found to be 96% (range 91-99%) with positive and 95% (89-98%) with neutral oral contrast; specificity was 96% (92-98%) and 94% (90-97%). For each reader, no statistically significant difference was found between the two agents (sensitivities p-values >0.6; specificities p-values>0.08), in the area under the ROC curve (range 0.95-0.99) or in average interpretation times. In cases without appendicitis, positive oral contrast demonstrated improved appendix identification (average 90% vs 78%) and higher confidence scores for three readers. Average interpretation times showed no statistically significant differences between the agents. CONCLUSION: Neutral vs positive oral contrast does not affect the accuracy of contrast-enhanced CT for diagnosing acute appendicitis. Although positive oral contrast might help to identify normal appendices, we continue to use neutral oral contrast given its other potential benefits.
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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 | 0.010 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".