Outcomes of cats treated with maxillectomy: 60 cases. A Veterinary Society of Surgical Oncology retrospective study
Bibliographic record
Abstract
Maxillectomy is poorly described for the management of oral tumours in cats and is occasionally not recommended because of the high complication rate and sub-optimal outcome reported in cats treated with mandibulectomy. The purpose of this study was to retrospectively evaluate the complications and oncologic outcome in cats treated with maxillectomy. Sixty cats were included in the study. Maxillectomy procedures included unilateral rostral (20.0%), bilateral rostral (23.3%), segmental (10.0%), caudal (20.0%) and total unilateral maxillectomy (26.7%). Intra-operative and post-operative complications were reported in 10 (16.7%) and 34 (56.7%) cats, respectively. The most common post-operative complications were hyporexia (20.0%) and incisional dehiscence (20.0%). The median duration of hyporexia was 7 days. Benign tumours were diagnosed in 19 cats (31.7%) and malignant tumours in 41 cats (68.3%). Local recurrence and metastatic rates were 18.3% and 4.9%, respectively; the median progression-free interval (PFI) was not reached. The disease-related median survival time was not reached overall or for either benign or malignant tumours. The 1- and 2-year survival rates were, respectively, 100% and 79% for cats with benign tumours, 89% and 89% for cats with malignant tumours, 94% and 94% for cats with fibrosarcomas, 83% and 83% for cats with squamous cell carcinomas, and 80% and 80% for cats with osteosarcomas. Poor prognostic factors included mitotic index for PFI, adjuvant chemotherapy for both PFI and survival time, and local recurrence for survival time. Maxillectomy is a viable treatment option for cats resulting in good local tumour control and long survival times.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".