CD8+ Tumor-Infiltrating Lymphocytes Together with CD4+ Tumor-Infiltrating Lymphocytes and Dendritic Cells Improve the Prognosis of Patients with Pancreatic Adenocarcinoma
Bibliographic record
Abstract
OBJECTIVE: Recent studies have demonstrated the importance of tumor immunity for a cancer patient's prognosis. In some types of cancer, it has been shown through immunohistochemical analysis that the existence of CD8+ tumor-infiltrating lymphocytes (TILs) is a crucial factor in determining prognosis. In an experimental model, CD4+ lymphocytes together with CD8+ lymphocytes contributed significantly to tumor immunity. METHODS: Specimens were taken from 80 surgically resected pancreatic adenocarcinomas between 1992 and 1999. Immunohistochemical staining of CD4, CD8, and S100 protein was performed, and the levels of these proteins were determined by microscopic analysis. The percentages of patients in the CD4(+) and CD8(+) groups were 59% (47/80) and 25% (16/80), respectively. When separated into 4 groups, CD4/8(+/+), CD4/8(+/-), CD4/8(-/+) and CD4/8(-/-), the overall survival rate was significantly higher in CD4/8(+/+) patients (13 cases) compared with those in all other groups combined (67 cases; P = 0.0098). CD4/8(+/+) status was negatively correlated with tumor depth and TNM stage. Multivariate analyses showed that CD4/8(+/+) status was an independent favorable prognostic factor. The number of tumor-infiltrating S100 protein positive cells was also significantly higher in the CD4/8(+/+) group than in others (P = 0.0084). CONCLUSIONS: In pancreatic adenocarcinoma, the presence of CD4+ TILs together with CD8+ TILs serves as a good indicator of the patient's outcome after surgical treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".