Anastomotic Leaks After Small- and Large-Bowel Surgery: Diagnostic Performance of CT and the Importance of Intraluminal Contrast Administration
Bibliographic record
Abstract
OBJECTIVE: The objective of our study was to evaluate the diagnostic performance of CT in the identification of anastomotic leaks. MATERIALS AND METHODS: This was a study of patients who underwent bowel surgery and a subsequent postoperative CT examination performed specifically for investigating for an anastomotic leak. The study group included patients with surgically confirmed anastomotic leaks (n = 59), and the control group included patients without anastomotic leaks (n = 48) confirmed by either repeat surgery or uneventful clinical follow-up for at least 6 months. Two radiologists and two radiology residents independently reviewed each CT examination for specific CT findings from a set of predetermined imaging predictors. The sensitivity and specificity for each imaging predictor were calculated for each reader, and the interobserver agreement was calculated using the Cohen kappa coefficient. Diagnostic performance was assessed using ROC curve analysis. RESULTS: The most sensitive imaging predictor was intraabdominal free fluid (95.3%). Leakage of intraluminal contrast agent was also a highly specific imaging predictor (96.6%). Substantial interobserver agreement was shown for intraabdominal free gas (κ = 0.76) and leakage of intraluminal contrast agent (κ = 0.76). Overall diagnostic performance in correctly identifying surgically confirmed leaks, as assessed by the area under the ROC curve, ranged from 0.76 to 0.86. Diagnostic performance was higher for all readers when intraluminal contrast agent was used and reached the anastomosis, with the exception of one reader, whose diagnostic performance remained unchanged. CONCLUSION: Diagnostic performance of CT was highest when an intraluminal contrast agent was used. Meticulous and careful use of an intraluminal contrast agent is therefore important in this patient population.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".