Impact of Tumor Size on the Survival Benefit of Anatomic Versus Non-Anatomic Resection for Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
BACKGROUND: The role of anatomic resection (AR) versus non-anatomic resection (NAR) for intrahepatic cholangiocarcinoma (ICC) has not been thoroughly investigated. This study sought to define the impact of tumor size on the relative therapeutic benefit of AR versus NAR for ICC. Specifically, the study aimed to identify a threshold tumor size to define when AR rather than NAR may be warranted to achieve better survival outcomes for patients undergoing resection of ICC. METHODS: Patients who underwent liver resection for ICC were identified from an international multi-institutional database. A multivariable Cox model with an interaction term was used to assess the relationship between tumor size and the survival impact of AR. RESULTS: Among 969 patients, 506 (72.9 %) underwent AR, whereas 263 (27.1 %) had an NAR. Multivariable analysis demonstrated an interaction between tumor size and AR (hazard ratio [HR], 0.94; 95 % confidence interval [CI], 0.88-1.00; p = 0.045). A plot of the interaction demonstrated that AR was associated with improved outcomes for tumors size ≥4 cm. Among 257 (26.5 %) patients with tumors smaller than 4 cm, recurrence-free survival (RFS) did not differ between NAR and AR (3-year RFS: 65.2 % [95 % CI, 55.7-76.2] vs 58.1 % [95 % CI, 49.2-68.5]; p = 0.720). In contrast, among 712 (73.4 %) patients with tumors size ≥4 cm, AR was associated with improved RFS (3-year RFS: 34.7 % [95 % CI, 27.5-43.8] vs 44.9 % [95 % CI, 40.4-50.0]; p = 0.018). CONCLUSIONS: Anatomic resection was associated with improved RFS for ICC patients with tumors size ≥4 cm, indicating that tumor size may be a valuable criterion to determine the extent of liver resection for resectable ICC patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".