Clinical features, treatment options, and outcome in dogs with thymoma: 116 cases (1999–2010)
Bibliographic record
Abstract
OBJECTIVE: To describe clinical signs, diagnostic findings, treatment, and outcome and determine factors associated with survival time for dogs with thymoma. DESIGN: Multi-institutional retrospective case series. ANIMALS: 116 dogs with thymoma. PROCEDURES: Medical records were searched for information regarding signalment, physical examination findings, results of laboratory testing and diagnostic imaging, medical and surgical treatment, and survival data. RESULTS: Of the 116 dogs with thymoma, 44 (38%) were Labrador Retrievers and Golden Retrievers. Twenty of 116 (17%) dogs had signs of myasthenia gravis (diagnosis was confirmed for 13 dogs). At the time of thymoma diagnosis, 40 (34%) dogs had hypercalcemia, 8 (7%) dogs had a concurrent immune-mediated disease, and 31 (27%) dogs had another tumor; 16 (14%) dogs developed a second nonthymic tumor at a later date. Tumor excision was performed for 84 dogs, after which 14 (17%) had tumor recurrence; prognosis was good for dogs undergoing a second surgery. Median survival time with and without surgical treatment was 635 and 76 days, respectively. Presence of another tumor at the time of thymoma diagnosis, lack of surgical excision, and higher pathological stage were significantly associated with shorter survival time. Hypercalcemia and presence of myasthenia gravis or megaesophagus at the time of thymoma diagnosis, histopathologic subtype of thymoma, or tumor development at a later date was not associated with survival time. CONCLUSIONS AND CLINICAL RELEVANCE: Dogs with thymoma, even those with a large tumor burden or a paraneoplastic syndrome, had a good prognosis following surgery. Surgical treatment, tumor stage, and the presence of a second tumor at diagnosis influenced survival time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".