Bibliographic record
Abstract
Cancer related deaths have shown a progressive increase over the past decade and the newer cases of cancers are estimated to rise in 2030. The current treatment modalities available for cancer are highly toxic, impair quality of life and develop resistance with course of time. Thus, there is a growing necessity for the prevention and cure of cancer related morbidity and mortality. One of the promising approaches for cancer prevention could be immunization with specific vaccines. The latest advances in immunology have led to the development of effective cancer vaccines to enhance immunity against tumour cells. Moreover, the occurrence of cancer with infectious agents like Hepatitis B virus (HBV) and Human Papilloma virus (HPV) as well as their prevention with specific cancer vaccines has further confirmed the role of immunotherapy in cancer. Though prophylactic vaccines are found to be more successful in cancer prevention, in the present scenario most of the vaccines under development are therapeutic cancer vaccines. Cancer vaccines stimulate the immune system and attack specific cancer cells without harming the normal cells. The major cancer vaccines under development to target tumour cells includes antigen vaccines, whole cell tumour vaccines, dendritic cell vaccine, viral vectors, DNA vaccines and idiotype vaccines. Apart from this, measures to produce patient-specific cancer vaccines from patients own tumour cells and a "universal" vaccine to provide immunity against cancer cells of any origin are being investigated. Hence this review gives an overview of various strategies involved in the development of cancer vaccines and the currently approved vaccines available for the prevention of cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".