Primary Renal Neoplasia of Dogs
Bibliographic record
Abstract
Abstract Background:Primary renal tumors are diagnosed uncommonly in dogs. Hypothesis:Signs and survival will differ among different categories of primary renal tumors. Animals:Data were collected from the medical records of 82 dogs with primary renal tumors diagnosed by examination of tissue obtained by ultrasound-guided biopsy, needle aspiration, surgery, or at postmortem examination. Methods:This was a multi-institutional, retrospective study. Results:Forty-nine dogs had carcinomas, 28 had sarcomas, and 5 had nephroblastomas. The dogs were geriatric (mean 8.1 years; range: 1–17) with a weight of 24.9 kg (range: 4.5–80). Tumors occurred with equal frequency in each kidney with 4% occurring bilaterally. Initial signs included one or more of hematuria, inappetance, lethargy, weight loss, or a palpable abdominal mass. Pain was reported more frequently in dogs with sarcomas (5/28). The most common hematologic abnormalities were neutrophilia (22/63), anemia (21/64), and thrombocytopenia (6/68). Polycythemia was present in 3 dogs and resolved with treatment. Hematuria (28/49), pyuria (26/49), proteinuria (24/50), and isosthenuria (20/56) were the most frequently observed abnormalities on urinalysis. Pulmonary metastases were noted on thoracic radiographs in 16% of dogs at diagnosis. Seventy-seven percent of dogs had metastatic disease at the time of death. Median survival for dogs with carcinomas was 16 months (range 0–59 months), for dogs with sarcomas 9 months (range 0–70 months), and for dogs with nephroblastomas 6 months (range 0–6 months). Conclusions and Clinical Importance: Primary renal tumors in dogs are generally highly malignant with surgery being the only treatment that improves survival.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".