Review of tracheal neoplasia in dogs and cats (1961–2024)
Bibliographic record
Abstract
Tracheal neoplasia is considered infrequent in domestic animals. A detailed summarized description of the demographic trends of the patients and frequent tumor types arising from this anatomical location is missing in the reference literature. To better describe clinical and pathological features, a multi-institutional retrospective analysis and literature review were conducted to collect all the tracheal neoplasms reported in dogs and cats. Forty-two cases from a multi-institutional data search and 123 documented cases from the veterinary literature between 1961 and August 2024 were collected for a total of 165 cases. Dogs represented 41.2% (68/165) of the cases retrieved, whereas the remaining 58.8% (97/165) were cats. The most common tracheal neoplasia in dogs in descending order were osteochondroma, plasma cell tumor, chondrosarcoma, malignant epithelial tumors (adenocarcinoma and carcinoma), and chondroma. In dogs, most of the affected animals were males (53%), with a mean age at the time of diagnosis of 6.7 years, and most of the tumors were located at the cervical trachea (43%). Labrador retrievers represented 10% of the cases. In cats, the most common tracheal neoplasms were lymphoma and malignant epithelial tumors (adenocarcinoma, carcinoma, and squamous cell carcinoma). In cats, most of the affected animals were males (52%) and domestic shorthairs (62%), with a mean age at the time of diagnosis of 10.7 years, and most of the tumors were located at the cervical trachea (38%). Diagnosticians must consider these differential diagnoses when dealing with tracheal samples that are suspicious of neoplasia in dogs and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".