Surgery and postoperative definitive radiotherapy for management of canine soft tissue sarcoma: a multi-institutional retrospective study of 272 dogs (2010–2020)
Bibliographic record
Abstract
OBJECTIVE: To report local progression and survival in dogs following surgery and postoperative definitive radiotherapy (dRT) for management of soft tissue sarcoma (STS) and to evaluate risk factors for local progression and survival. METHODS: Records were retrospectively reviewed at 9 referral hospitals for dogs managed with postoperative dRT between January 1, 2010, and January 1, 2020, following surgery for STS. Data related to presentation, surgery, dRT, systemic therapy, and outcome were abstracted. Selected variables were assessed for association with local progression and overall survival. RESULTS: 272 dogs were included. Histologic grade was reported in 249 dogs: 102 were grade 1 (40.9%), 120 were grade 2 (48.2%), and 27 were grade 3 (10.8%). Local progression was suspected or confirmed in 56 dogs. Local progression rates were similar for grade 1 (24 of 89 [26.7%]), grade 2 (23 of 111 [20.7%]), and grade 3 tumors (6 of 22 [27.3%]). Previous recurrence (P = .010) and subsequent distant metastasis (P = .014) were associated with more frequent local progression; intensity-modulated radiotherapy was associated with decreased local progression (P = .025) compared to other forms of delivery. Age (P = .049), grade (P = .009), previous recurrence (P = .009), and institution type for surgery (P = .043) were associated with overall survival. CONCLUSIONS: Outcomes for most dogs were good; however, the frequency of local progression indicates an ongoing need to critically appraise local management strategies, particularly for low-grade STS. Intensity-modulated radiotherapy was associated with lower rates of local progression and may be preferred to less precise forms of delivery. CLINICAL RELEVANCE: These data may guide clinicians when making decisions regarding dRT for management of STS.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".