Development and Validation of a Laboratory Risk Score (LabScore) to Predict Outcomes after Resection for Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND: Estimating prognosis in the preoperative setting is challenging, as most survival risk scores rely exclusively on postoperative factors. We sought to develop a composite score that incorporated preoperative liver, tumor, nutritional, and inflammatory markers to predict long-term outcomes after resection of intrahepatic cholangiocarcinoma (ICC). STUDY DESIGN: Patients who underwent curative-intent hepatectomy for ICC between 2000 and 2017 were identified using an international multi-institutional database. Clinicopathologic factors were assessed using bivariate and multivariable analysis and a prognostic model to estimate overall survival (OS) based only on preoperative laboratory values (LabScore) was developed and validated. RESULTS: Among 660 patients, median OS was 43.2 months and 5-year OS rate was 42.4%. On multivariable analysis, laboratory values associated with OS included carbohydrate antigen 19-9 (hazard ratio [HR] 1.16; 95% CI 1.05 to 1.27), neutrophil-to-lymphocyte ratio (HR 1.09; 95% CI, 1.05 to 1.13), platelet count (HR 1.01; 95% CI, 1.00 to 1.01), and albumin (HR 0.75; 95% CI, 0.62 to 0.92). A weighted LabScore was constructed based on the formula: (8.2 + 1.45 × natural logarithm of carbohydrate antigen 19-9 + 0.84 × neutrophil-to-lymphocyte ratio + 0.03 × platelets - 2.83 × albumin). Patients with a LabScore of 0 to 9 (n = 223), 10 to 19 (n = 353) and ≥20 (n = 88) had incrementally worse 5-year OS rates of 54.9%, 38.2% and 21.6%, respectively (p < 0.001). The model demonstrated good performance in both the test (c-index 0.70) and validation cohorts (c-index 0.67), as well as outperformed individual laboratory markers, the prognostic nutritional index (c-index 0.58), and American Joint Committee on Cancer staging system (c-index 0.60). CONCLUSIONS: A preoperative LabScore was able to predict long-term outcomes of patients after resection for ICC better than American Joint Committee on Cancer staging system. The LabScore can be used to preoperatively identify patients who will benefit the most from upfront operation or alternative treatment options, including neoadjuvant chemotherapy before resection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".