Adoptive immunotherapy against ovarian cancer
Bibliographic record
Abstract
The standard front-line therapy for epithelial ovarian cancer (EOC) is combination of debulking surgery and platinum-based chemotherapy. Nevertheless, the majority of patients experience disease recurrence. Although extensive efforts to find new therapeutic options, cancer cells invariably develop drug resistance and disease progression. New therapeutic strategies are needed to improve prognosis of patients with advanced EOC.Recently, several preclinical and clinical studies investigated feasibility and activity of adoptive immunotherapy in EOC. Our aim is to highlight prospective of adoptive immunotherapy in EOC, focusing on HLA-restricted Tumor Infiltrating Lymphocytes (TILs), and MHC-independent immune effectors such as natural killer (NK), and cytokine-induced killer (CIK). Adoptive cell therapy (ACT) has shown activity in several pre-clinical models. Available preclinical and clinical data suggest that adoptive cell therapy may provide the best benefit in settings of low tumor burden, minimal residual disease, or maintenance therapy. Further studies are needed to better define the optimal clinical setting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".