Surgery With Arterial Resection for Hilar Cholangiocarcinoma: Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: In light of recent advances in multimodality treatment, an analysis of vascular resection outcomes in surgery for hilar cholangiocarcinoma is lacking. OBJECTIVE: The aim of this meta-analysis is to summarize the currently available evidence on outcomes of patients undergoing arterial resection for the treatment of hilar cholangiocarcinoma. METHODS: A systematic literature search in the databases PubMed/MEDLINE, Cochrane Library, and CINAHL, and the trial registries ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform will be carried out. Predefined outcomes are mortality (100-day and in-hospital), morbidity (Clavien-Dindo classification, any type of complication), vascular complications (thrombosis or stenosis of the portal vein or hepatic artery, pseudoaneurysms), liver failure, postoperative bleeding, duration of surgery, reoperation rate, length of hospital stay, survival time, actuarial survival (2-, 3-, and 5-year survival), complete/incomplete resection rates, histologic arterial invasion, and lymph node positivity (number of positive lymph nodes and lymph node ratio). RESULTS: Database searches will commence in December 2020. The meta-analysis will be completed by December 2021. CONCLUSIONS: Our findings will enable us to present the current evidence on the feasibility, safety, and oncological effectiveness of surgery for hilar cholangiocarcinoma with arterial resection. Our data will support health care professionals and patients in their clinical decision-making. TRIAL REGISTRATION: PROSPERO 223396; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=223396. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31212.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".