Adjuvant chemotherapy improves oncological outcomes of resectable intrahepatic cholangiocarcinoma
Bibliographic record
Abstract
OBJECTIVE: To define the role of adjuvant chemotherapy in the management of resectable intrahepatic cholangiocarcinoma (ICC) by performing a meta-analysis. SUMMARY BACKGROUND DATA: Oncological benefit of adjuvant chemotherapy in resectable ICC remains controversial, high-level evidence in such context is lacking. METHOD: A comprehensive search using Pubmed, EMbase, and Web of Science was performed from inception to October 2018. Studies compared the survival of patients receiving adjuvant chemotherapy versus surgery alone were included. Data were analyzed using random effect model. Quality of each study and presence of publication bias were assessed by Newcastle-Ottawa score (NOS) and funnel plot with Egger test respectively. RESULTS: The present meta-analysis included 15 studies (all were retrospective series) and 5060 patients. Adjuvant chemotherapy was administered either intravenously or intra-arterially in the form of trans-arterial chemo-embolization (TACE). The average NOS for the included studies was 6.5. Pooled analysis of the included studies demonstrated significant advantage in the adjuvant chemotherapy group (HR 0.66, 0.55-079, P <.001, I-square [I] = 20.8%). After 2 studies were removed for heterogeneity, advantage of adjuvant chemotherapy remained (HR 0.72, 0.62-0.84, P <.001, I = 0%). Funnel plot suggested no significant publication bias (Egger test, 2-tailed P = .203). Subgroup analyses suggested that intravenous route of chemotherapy injection (P <.001) and use of gemcitabine base regimen (P = .004) are associated with improved overall survival. Adjuvant chemotherapy did not improve disease-free survival in subgroup analysis (P = .94). CONCLUSION: Adjuvant chemotherapy is associated with improved overall survival and should be considered in patients with ICC following curative resection and in particular to patients with advance 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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".