Review of neoplasia in fish at a large display aquarium, 2005–2021
Bibliographic record
Abstract
Fish maintained in managed care may have longer lifespans as a result of advances in veterinary medicine and husbandry and reduced risk of predation. Neoplasia is of increasing interest in managed aquarium populations. However, few studies have systematically evaluated neoplasia in managed fish populations. Our objective in this retrospective study was to review and describe neoplasia diagnosed in fish at a large public display aquarium between 2005 and 2021. Any fish diagnosed with neoplasia on either antemortem or postmortem evaluation during the study period was included, and all medical records, biopsy, and autopsy reports were reviewed. Sixty-two fish met the inclusion criteria; 37 species were included in the study population, most of which were tropical freshwater fish ( n = 34 fish). Thirty-two types of neoplasia were identified. Ten fish had benign neoplasms, and 53 fish had malignant neoplasms. The most common neoplasms were of epithelial and neuroectodermal origin. The most common site of tumor origin was the skin. Our data suggest that mesenchymal neoplasms may be more common in cold saltwater fish than in tropical freshwater and saltwater fish. Malignant neoplasms were most commonly diagnosed in the study population and should be a top differential when neoplasms are identified in fish managed under human care. Our study contributes to the overall knowledge of the health of aquarium fish and may aid clinicians in characterizing neoplasia that may be present in fish under human care.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".