Feline Cancer Prevalence in South Africa (1998 – 2005): Contrasts with the Rest of the World
Bibliographic record
Abstract
A paucity of information exists on the relative proportions, incidences or outcomes of diagnosis and treatment of feline cancer in South Africa. Standard texts of veterinary oncology quote data from the Northern hemisphere, and geographic differences are apparent. In this retrospective analysis, the electronic medical database of the Onderstepoort Veterinary Academic Hospital was analysed for feline cancer felines admissions for the period 1998 – 2005 (n = 100 out of N = 12,893 feline admissions, or 0.78% of total feline admissions). The average and median age of feline cancer felines was 7 and 9.5 years respectively. In contrast to published reports of US, Australian and European data where lymphosarcoma is the most common cancer affecting cats, squamous cell carcinoma (SCC) forms the predominant neoplasm (48% of all tumours). White or part-white cats were overrepresented in this group, which is consistent with greater ultraviolet light exposure. Lymphoma was the second most common diagnosis, followed by various carcinomas and adenocarcinomas. A large proportion (54%) of felines received some form of treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".