Perihilar Cholangiocarcinoma – Novel Benchmark Values for Surgical and Oncological Outcomes From 24 Expert Centers
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Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to define robust benchmark values for the surgical treatment of perihilar cholangiocarcinomas (PHC) to enable unbiased comparisons. BACKGROUND: Despite ongoing efforts, postoperative mortality and morbidity remains high after complex liver surgery for PHC. Benchmark data of best achievable results in surgical PHC treatment are however still lacking. METHODS: This study analyzed consecutive patients undergoing major liver surgery for PHC in 24 high-volume centers in 3 continents over the recent 5-year period (2014-2018) with a minimum follow-up of 1 year in each patient. Benchmark patients were those operated at high-volume centers (≥50 cases during the study period) without the need for vascular reconstruction due to tumor invasion, or the presence of significant co-morbidities such as severe obesity (body mass index ≥35), diabetes, or cardiovascular diseases. Benchmark cutoff values were derived from the 75th or 25th percentile of the median values of all benchmark centers. RESULTS: Seven hundred eight (39%) of a total of 1829 consecutive patients qualified as benchmark cases. Benchmark cut-offs included: R0 resection ≥57%, postoperative liver failure (International Study Group of Liver Surgery): ≤35%; in-hospital and 3-month mortality rates ≤8% and ≤13%, respectively; 3-month grade 3 complications and the CCI: ≤70% and ≤30.5, respectively; bile leak-rate: ≤47% and 5-year overall survival of ≥39.7%. Centers operating mostly on complex cases disclosed better outcome including lower post-operative liver failure rates (4% vs 13%; P = 0.002). Centers from Asia disclosed better outcomes. CONCLUSION: Surgery for PHC remains associated with high morbidity and mortality with now the availability of benchmark values covering 21 outcome parameters, which may serve as key references for comparison in any future analyses of individuals, group of patients or centers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it