Exploring animal models in oral cancer research and clinical intervention: A critical review
Bibliographic record
Abstract
Cancer is a leading cause of death worldwide, but advances in treatment, early detection, and prevention have helped to reduce its impact. To translate cancer research findings into clinical interventions for patients, appropriate animal experimental models, particularly in oral cancer therapy, can be helpful. In vitro experiments using animal or human cells can provide insight into cancer's biochemical pathways. This review discusses the various animal models used in recent years for research and clinical intervention in oral cancer, along with their advantages and disadvantages. We highlight the advantages and limitations of the used animal models in oral cancer research and therapy by searching the terms of animal models, oral cancer, oral cancer therapy, oral cancer research, and animals to find all relevant publications during 2010-2023. Mouse models, widely used in cancer research, can help us understand protein and gene functions in vivo and molecular pathways more deeply. To induce cancer in rodents, xenografts are often used, but companion animals with spontaneous tumours are underutilized for rapid advancement in human and veterinary cancer treatments. Like humans with cancer, companion animals exhibit biological behaviour, treatment responses, and cytotoxic agent responses similar to humans. In companion animal models, disease progression is more rapid, and the animals have a shorter lifespan. Animal models allow researchers to study how immune cells interact with cancer cells and how they can be targeted specifically. Additionally, animal models have been extensively used in research on oral cancers, so researchers can use existing knowledge and tools to better understand oral cancers using animal models.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".