Stereotactic body radiation therapy for treatment of soft tissue sarcomas in 35 dogs
Bibliographic record
Abstract
OBJECTIVE: To describe response rate, tumor progression, patient survival times, prognostic factors associated with tumor progression and patient survival times, and radiation toxicoses (acute and latent) in dogs treated with curative-intent stereotactic body radiation therapy (SBRT) for soft tissue sarcomas (STS). ANIMALS: 35 client-owned dogs with STS treated with curative-intent SBRT between October 2011 and May 2017. PROCEDURES: Medical records were reviewed to identify dogs that underwent SBRT. Dogs with oral tumors, hemangiosarcoma, or histiocytic sarcoma were excluded. Data collected included patient-, STS-, and SBRT-related information, including follow-up information pertaining to tumor progression and patient survival time for ≥ 6 months, unless tumor progression or patient death occurred sooner. RESULTS: Objective measurements allowing for evaluation of tumor response were available for 28 dogs, of which 13 (46%) had either a partial (10/28 [36%]) or complete (3/28 [11%]) response. Twenty-four dogs died, and the medians for progression-free survival time, time to progression of disease, overall survival time, and disease-specific survival time were 521, 705, 713, and 1,149 days, respectively. Low histologic grade and extremity locations of STSs were positive prognostic factors for patient survival times. Acute adverse effects were limited to skin, and 1 dog underwent limb amputation because of a nonhealing wound. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that SBRT for STS was well tolerated in most dogs and provided local tumor control. Additional studies are needed to determine the best SBRT protocol for treatment of STSs in dogs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".