Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past several decades, immunotherapy as a novel cancer treatment has made tremendous progress. Several categories of immunotherapy have emerged, all designed to stimulate the patients’ immune system to fight cancer. Cancer vaccines, specifically virus-based cancer vaccines, is a subcategory of immunotherapy that can trigger both innate and adaptive immune response by targeting tumor-specific and tumor-associated antigens. This review summarizes the mechanisms of different types of virus-based cancer vaccines, including inactivated/live attenuated/subunit vaccines, oncolytic virus vaccines, and viral vector vaccines. Furthermore, this review introduces the clinical applications of virus-based cancer vaccines, including oncolytic virus vaccine T-VEC against metastasized melanoma, and Pexa-Vec and PROSTVAC that are currently in clinical trials. Virus-based cancer vaccines have shown encouraging results in numerous studies in enhancing overall survival, relapse-free survival, and overall response rate among patients with solid tumors. This review underscores the necessity for future research aimed at improving the efficacy of virus-based cancer vaccines and investigating combination therapies with other immunotherapies to achieve optimal treatment outcomes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it