Photodynamic Therapy or Palliative Resection Versus Biliary Stenting for Advanced Cholangiocarcinoma: A Network Meta-Analysis
Bibliographic record
Abstract
Background:We assessed the efficacy of evaluating palliative resection (R1/R2 resection), photodynamic therapy (PDT), and biliary stenting (stent) alone in the treatment of advanced cholangiocarcinoma using a reticulated meta-analysis. Methods:A systematical retrieval in PubMed, Web of Science, and the Cochrane Library was performed for relative literature on the effects of PDT, palliative resection, and simple biliary stenting in the treatment of advanced cholangiocarcinoma. A literature search updated to January 30, 2024, was performed. Newcastle-Ottawa Scale and Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tools were used to assess the methodological quality of the included studies, and Addis-1.16 0.6 software for reticulated meta-analysis. Results:The results showed that PDT and palliative resection were superior to biliary stenting alone in improving 1-, 2-, and 3-year survivals, and the incidence of cholangitis. The relative effectiveness of PDT, palliative resection, and biliary stent placement alone in improving survival was as follows: PDT, palliative resection, and biliary stent placement alone. Conclusion:There is no significant difference between PDT and palliative resection in improving patient survival time, and PDT does not increase the incidence of cholangitis.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".