Clinical and Immunologic Evaluation of Dendritic Cell–Based Immunotherapy in Combination With Gemcitabine and/or S-1 in Patients With Advanced Pancreatic Carcinoma
Bibliographic record
Abstract
OBJECTIVES: In the current study, we have evaluated the clinical and immunological responses in patients with advanced pancreatic carcinoma who received dendritic cell (DC)-based immunotherapy in combination with gemcitabine and/or S-1. METHODS: Dendritic cell-based immunotherapy (DC vaccine alone or DC vaccine plus lymphokine-activated killer [LAK] cell therapy) in combination with gemcitabine and/or S-1 has been carried out in 49 patients with inoperable pancreatic carcinoma refractory to standard treatment. RESULTS: Of 49 patients, 2 patients had complete remission, 5 had partial remission, and 10 had stable disease. Prolongation of survival in this cohort was highly likely (median survival, 360 days). Survival of patients receiving DC vaccine and chemotherapy plus LAK cell therapy was longer than those receiving DC vaccine in combination with chemotherapy but no LAK cells. Increased numbers of cancer antigen-specific cytotoxic T cells and decreased regulatory T cells were observed in several patients on immunotherapy, but increased overall survival time tended to be associated only with the latter. None of the patients experienced grade 3 or worse adverse events during the treatment period. CONCLUSIONS: Dendritic cell vaccine-based immunotherapy combined with chemotherapy was shown to be safe and possibly effective in patients with advanced pancreatic cancer refractory to standard treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 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".