Predicting Acute Cholecystitis on Final Pathology to Prioritize Surgical Urgency: An Evaluation of the Tokyo Criteria and Development of a Novel Predictive Score
Bibliographic record
Abstract
INTRODUCTION: Limited operating room access in publicly funded institutions, particularly for emergencies, highlights the need for improved prioritization rules for common pathologies, including acute cholecystitis (AC). We compared the performance of our institution's surgical prioritization rules to the Tokyo diagnostic criteria and to develop a novel decision rule to predict AC on surgical pathology. METHODS: All adult patients undergoing emergency cholecystectomy (EC) at a single academic institution (2017-2021) were reviewed. The primary outcome was the diagnosis of acute inflammation on final pathologic analysis. Multiple logistic regression was performed with a training subset using relevant clinical variables that were selected a priori. A simple weighted decision rule was created and compared to the Tokyo Guidelines and the institution's existing prioritization rules via an analysis of receiver operator curves. RESULTS: Among 756 patients undergoing EC, 97.6% met the criteria for AC as per Tokyo diagnostic criteria. Urgent booking category (area under the curve [AUC] = 0.58, sensitivity 89%, specificity 26%) and Tokyo criteria (AUC = 0.51, sensitivity 99%, specificity 3%) poorly discriminated for acute inflammation on final pathology. Discrimination of the hospital's case prioritization rules was moderate (AUC = 0.63, sensitivity 48%, specificity 78%), and a new decision rule was significantly higher (AUC = 0.71, sensitivity 72%, specificity 64%, P < 0.003). CONCLUSIONS: The Tokyo Guidelines were highly sensitive but nonspecific for the presence of acute inflammation on final pathology. An existing institutional case prioritization rule demonstrated moderate discrimination but was outperformed by a new rule incorporating clinical exam, fever, degree of leukocytosis, and inflammatory changes on imaging. These findings may aid AC prioritization in busy centers, though external validation is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".