Does ultrasongraphy predict intraoperative findings at cholecystectomy? An institutional review
Bibliographic record
Abstract
BACKGROUND: Ultrasonography (US) is the mainstay of biliary tract imaging, but few recent studies have tested its ability to diagnose acute cholecystitis (AC). Our objective was to determine how well a US diagnosis of AC correlates with the intraoperative diagnosis. We hypothesize that US underestimates this diagnosis, potentially leading to unexpected findings in the operating room (OR). METHODS: This retrospective review included all patients admitted to the acute care surgical service of a tertiary hospital in 2011 with suspected biliary pathology who underwent US and subsequent cholecystectomy. We determined the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of US using the intraoperative diagnosis as the gold standard. Further analysis identified which US findings were most predictive of an intraoperative diagnosis of AC. We used a recursive partitioning method with random forests to identify unique combinations of US findings that, together, are most predictive of AC. RESULTS: In total, 254 patients underwent US for biliary symptoms; 152 had AC diagnosed, and 143 (94%) of them underwent emergency surgery (median time to OR 23.03 hr). Ultrasonography predicted intraoperative findings with a sensitivity of 73.2%, specificity of 85.5% and PPV of 93.7%. The NPV (52.0%) was quite low. The US indicators most predictive of AC were a thick wall, a positive sonographic Murphy sign and cholelithiasis. Recursive partitioning demonstrated that a positive sonographic Murphy sign is highly predictive of intraoperative AC. CONCLUSION: Ultrasonography is highly sensitive and specific for diagnosing AC. The poor NPV confirms our hypothesis that US can underestimate AC.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".