Risk factors and outcome in dogs with recurrent massive hepatocellular carcinoma: A <scp>Veterinary Society of Surgical Oncology</scp> case–control study
Bibliographic record
Abstract
Local recurrence after surgical excision of canine massive hepatocellular carcinoma (HCC) has been poorly studied in veterinary medicine with scant information published regarding risk factors for and outcome following recurrence. The aim of this case-control study was to describe the time to recurrence, evaluate potential risk factors for recurrence, and report the outcome in dogs with massive HCC. Medical records for 75 dogs who developed recurrence and 113 dogs who did not develop recurrence were reviewed. Statistical analyses were performed to determine risk factors for recurrence as well as the median time to develop recurrence and overall survival time (OS). None of the risk factors evaluated were significant for the development of recurrence. The median time to develop recurrence was 367 days (range 32-2096 days). There was no significant difference in median OS for dogs who developed recurrence vs. those who did not (851 vs. 970 days). For dogs with recurrent HCC, treatment at recurrence trended toward prolonged OS but was not significantly different from dogs not undergoing treatment at recurrence. There was no significant difference in median OS for dogs with histologically complete vs. incomplete tumour excision (990 vs. 903 days). Although specific risk factors for recurrence were not identified, elevations in liver values were noted in patients with recurrent disease and could act as a noninvasive surveillance tool. Recurrence was noted earlier in dogs who had routine post-operative surveillance (228 vs. 367 days). Routine surveillance for recurrence is recommended especially in dogs where further intervention is possible and should extend beyond 1 year. Patients with massive HCC have a good long-term prognosis regardless of incomplete excision, pulmonary metastasis, or recurrent local disease.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".