Development of a Predictive Model for Common Bile Duct Stones in Patients With Clinical Suspicion of Choledocholithiasis: A Cohort Study
Bibliographic record
Abstract
Background: Current choledocholithiasis guidelines heavily focus on patients with low or no risk, they may be inappropriate for populations with high rates of choledocholithiasis. We aimed to develop a predictive scoring model for choledocholithiasis in patients with relevant clinical manifestations. Methods: A multivariable predictive model development study based on a retrospective cohort of patients with clinical suspicion of choledocholithiasis was used in this study. The setting was a 700-bed public tertiary hospital. Participants were patients who had completed three reference tests (endoscopic retrograde cholangiography, magnetic resonance cholangiopancreatography, and intraoperative cholangiography) from January 2019 to June 2021. The model was developed using logistic regression analysis. Predictor selection was conducted using a backward stepwise approach. Three risk groups were considered. Model performance was evaluated by area under the receiver operating characteristic curve, calibration, classification measures, and decision curve analyses. Results: Six hundred twenty-one patients were included; the choledocholithiasis prevalence was 59.9%. The predictors were age > 55 years, pancreatitis, cholangitis, cirrhosis, alkaline phosphatase level of 125 - 250 or > 250 U/L, total bilirubin level > 4 mg/dL, common bile duct size > 6 mm, and common bile duct stone detection. Pancreatitis and cirrhosis each had a negative score. The sum of scores was -4.5 to 28.5. Patients were categorized into three risk groups: low-intermediate (score ≤ 5), intermediate (score 5.5 - 14.5), and high (score ≥ 15). Positive likelihood ratios were 0.16 and 3.47 in the low-intermediate and high-risk groups, respectively. The model had an area under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.76, 0.83) and was well-calibrated; it exhibited better statistical suitability to the high-prevalence population, compared to current guidelines. Conclusions: Our scoring model had good predictive ability for choledocholithiasis in patients with relevant clinical manifestations. Consideration of other factors is necessary for clinical application, particularly regarding the availability of expert physicians and specialized equipment.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".