Age, operation time and surgical approach can be used to detect incidental gallbladder carcinoma in cholecystectomy specimens from low‐incidence settings
Bibliographic record
Abstract
AIMS: Gallbladders resected for non-neoplastic diseases are systemically examined microscopically to rule out incidental dysplasia and carcinoma. The main aim of this study was to test whether a pre-grossing algorithm can detect incidental gallbladder carcinoma. The secondary aim was to test whether the algorithm can detect high-grade dysplasia. METHODS AND RESULTS: A retrospective study of clinical, pathological and radiological findings in cholecystectomy recipients was performed on a test set to develop a classification and regression tree algorithm. Cholecystectomy cases were included; exclusion criteria were age <18 years, missing pathology reports, preoperative suspicion of neoplastic disease, and cholecystectomy for non-gallbladder oncological disease. Five thousand nine hundred and eighty-two cholecystectomies from 2006 to 2018 were included in the study, with 18 cases of incidental gallbladder carcinoma and 11 cases of high-grade dysplasia. Three hundred and ninety controls were randomly selected for the testing set. Patient age, surgical approach, operation duration, dilatation of the biliary tract and gallbladder gross anomalies were statistically significant distinguishing factors in multivariate analysis (P < 0.00-0.026). Unsupervised testing with a conditional inference tree suggested that age, procedure type and operation duration can be used to identify incidental gallbladder carcinoma from controls, whereas high-grade dysplasia also requires grossing parameters to identify half of the cases (5/11). CONCLUSION: Readily available clinical parameters and postoperative data can be used to detect incidental gallbladder carcinoma. High-grade dysplasia mostly requires grossing and microscopic examination.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".