Clinical outcome of 42 dogs with scapular tumors treated by scapulectomy: A Veterinary Society of Surgical Oncology (<scp>VSSO</scp>) retrospective study (1995–2010)
Bibliographic record
Abstract
OBJECTIVE: To report signalment, clinical signs, preoperative staging tests, histologic diagnosis, surgical, and oncologic outcomes including postoperative limb use, in dogs with scapular tumors treated by scapulectomy. STUDY DESIGN: Retrospective case series ANIMALS: Dogs (n = 42) with scapular tumors. METHODS: Medical records (1995-2010) from 6 hospitals were searched for dogs with scapular tumors treated by scapulectomy. Data retrieved were: signalment, weight, percentage of scapula removed, histologic diagnosis, postoperative limb use, adjunctive therapy, disease free interval (DFI), and survival time (ST). Individual variables were modeled with a Cox proportional hazard model accounting for censoring to determine risk factors for decreased DFI and ST. For categorical variables, Kaplan-Meier survival plots as well as mean and median survival times (MSTs) were calculated. RESULTS: Subtotal scapulectomy was performed in 18 dogs (42.9%). Osteosarcoma (OSA) was diagnosed in 27 dogs (64.3%). Limb use was evaluated immediately after surgery in 41 dogs. Information on limb use at other times (1, 2, 3, and >3 months) postoperatively was also available for some dogs and was good to excellent overall. Only adjunctive chemotherapy had a positive significant effect on DFI (P = .00011) and ST (P = .0003). CONCLUSION: Canine scapular tumors can be treated effectively by scapulectomy and limb use is fair to excellent for most dogs. OSA was the most common scapular tumor. Overall prognosis for scapular OSA is similar to appendicular OSA at other sites and use of adjunctive chemotherapy prolonged the overall DFI and MST.
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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 | 0.001 |
| 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".