Lymph Node Staging in Perihilar Cholangiocarcinoma: The Key to the Big Picture
Bibliographic record
Abstract
Klatskin tumors have a bad prognosis despite aggressive therapy. The role and extent of lymph node dissection during surgery is a matter of discussion. This retrospective study analyzes our current experience of surgical treatments in the last decade. Patients and Methods: A retrospective single-center analysis of patients (n = 317) who underwent surgical treatment for Klatskin tumors. Univariable and multivariable logistic regression and Cox proportional analysis were performed. The primary endpoint was to investigate the role of lymph node metastasis for patient survival after complete tumor resection. The secondary endpoint was the prediction of lymph node status and long-term survival from preoperatively available parameters. Results: In patients with negative resection margins, a negative lymph node status was the prognosis-determining factor with a 1-, 3-, and 5-year survival rate of 87.7%, 37%, and 26.4% compared with 69.5%, 13.9%, and 9.3% for lymph-node-positive patients, respectively. Multivariable logistic regression for complete resection and negative lymph node status demonstrated only Bismuth type 4 (p = 0.01) and tumor grading (p = 0.002) as independent predictors. In multivariate Cox regression analysis, independent predictors of survival after surgery were the preoperative bilirubin level (p = 0.03), intraoperative transfusion (p = 0.002), and tumor grading (G) (p = 0.001). Conclusion: Lymph node dissection is of utmost importance for adequate staging in patients undergoing surgery for perihilar cholangiocarcinoma. In spite of extensive surgery, long-term survival is clearly associated with the aggressiveness of the disease.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".