A RETROSPECTIVE STUDY OF NEOPLASIA IN NONDOMESTIC FELIDS IN HUMAN CARE, WITH A COMPARATIVE LITERATURE REVIEW
Bibliographic record
Abstract
This retrospective study of neoplasia in nondomestic felids in human care presents the cases diagnosed at Northwest ZooPath (NWZP), Monroe, Washington, from 1998 to 2017 in conjunction with a scoping literature review. The 554 neoplasms identified in 20 species in the NWZP archive were combined with the 984 neoplasms identified in those same species in the published literature. Some of the cases identified in the literature were from the NWZP archive. Based on this review, mammary adenocarcinoma (183/1,483, 12.3%), lymphoma (89/1,483, 6.0%), squamous cell carcinoma (85/1,483, 5.7%), pheochromocytoma (57/1,483, 3.8%), and thyroid adenoma (57/1,483, 3.8%) are the most frequently reported neoplasms in nondomestic felids in human care. Apparent species predilections for neoplasia include mammary adenocarcinoma in tigers, jaguars, lions, and jungle cats; lymphoma in lions and tigers; squamous cell carcinoma in snow leopards; pheochromocytoma in clouded leopards; ovarian adenocarcinoma in jaguars; cholangiocarcinoma in lions and tigers; multiple myeloma in tigers; bronchoalveolar adenocarcinoma in cougars and lions; hemangiosarcoma, hepatocellular carcinoma, and gastrointestinal adenocarcinoma in lions; mesothelioma in clouded leopards, lions, and tigers; myelolipoma and cutaneous mast cell tumor in cheetahs; soft tissue sarcomas in tigers; and transitional cell carcinoma of the urinary bladder in fishing cats.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".