Papillomaviruses and Papillomaviral Disease in Dogs and Cats: A Comprehensive Review
Bibliographic record
Abstract
Papillomaviruses (PVs) frequently infect humans as well as non-human species. While most PV infections are asymptomatic, PVs can also cause hyperplastic papillomas (warts) as well as pre-neoplastic and neoplastic lesions. In this review, the life cycle of PVs is discussed, along with the mechanisms by which PVs cause hyperplastic and neoplastic diseases. The humoral and cell-mediated immune responses to PVs are reviewed, giving context to the later discussion on the use of vaccines to reduce canine and feline PV-associated disease. Both dogs and cats are infected by numerous different PV types classified into multiple different PV genera. The taxonomic classification of PVs is reviewed, along with the significance of this classification. The PV-associated diseases of dogs and cats are then described. These descriptions include the clinical presentation of the disease, the causative PV types, the histological features that allow diagnosis, and, where appropriate, possible treatment options. The review is comprehensive and contains the latest information about PVs and the diseases they cause in dogs and cats.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".