Treatment of Canine Hemangiosarcoma: 2000 and Beyond
Bibliographic record
Abstract
Canine hemangiosarcoma (HSA) is an aggressive and malignant neoplasia with a grave prognosis. Surgery and chemotherapy have limited success in prolonging survival times and increasing quality of life in dogs with HSA. Advances in medical oncology are resulting in increased survival rates and a better quality of life for veterinary cancer patients. An understanding of mechanisms of metastasis has led to the development of new treatments designed to delay or inhibit tumor spread. Promising new treatment options include novel delivery systems (inhalation or intracavitary chemotherapy); use of immunomodulators such as liposome-encapsulated muramyl tripeptide-phosphatidylethanolamine; antimetastatic agents such as inhibitors of angiogenesis (interferons, thalidomide), matrix metalloproteinase inhibitors, and minocycline; dietary modifications; and gene therapy. Inhibitors of angiogenesis seem to be safe and, unlike conventional chemotherapy, do not induce drug resistance. Although many of the newer approaches are still under development and review, the use of multimodality therapy incorporating innovative treatment modalities may offer the best therapeutic option for dogs affected with HSA.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".