Distribution of histopathologic types of primary pulmonary neoplasia in dogs and outcome of affected dogs: 340 cases (2010–2019)
Bibliographic record
Abstract
OBJECTIVE: To provide updated information on the distribution of histopathologic types of primary pulmonary neoplasia in dogs and evaluate the effect of postoperative adjuvant chemotherapy in dogs with pulmonary carcinoma. ANIMALS: 340 dogs. PROCEDURES: Medical records of dogs that underwent lung lobectomy for removal of a primary pulmonary mass were reviewed, and histopathologic type of lesions was determined. The canine lung carcinoma stage classification system was used to determine clinical stage for dogs with pulmonary carcinoma. RESULTS: Pulmonary carcinoma was the most frequently encountered tumor type (296/340 [87.1%]), followed by sarcoma (26 [7.6%]), adenoma (11 [3.2%]), and pulmonary neuroendocrine tumor (5 [1.5%]); there was also 1 plasmacytoma and 1 carcinosarcoma. Twenty (5.9%) sarcomas were classified as primary pulmonary histiocytic sarcoma. There was a significant difference in median survival time between dogs with pulmonary carcinomas (399 days), dogs with histiocytic sarcomas (300 days), and dogs with neuroendocrine tumors (498 days). When dogs with pulmonary carcinomas were grouped on the basis of clinical stage, there were no significant differences in median survival time between dogs that did and did not receive adjuvant chemotherapy. CLINICAL RELEVANCE: Results indicated that pulmonary carcinoma is the most common cause of primary pulmonary neoplasia in dogs; however, nonepithelial tumors can occur. Survival times were significantly different between dogs with pulmonary carcinoma, histiocytic sarcoma, and neuroendocrine tumor, emphasizing the importance of recognizing the relative incidence of these various histologic diagnoses. The therapeutic effect of adjuvant chemotherapy in dogs with pulmonary carcinoma remains unclear and warrants further investigation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".