Traditional Chinese Veterinary Medicine to Treat Oral Cancer in a Labrador Retriever
Bibliographic record
Abstract
A 5-year-old male Labrador retriever was presented for traditional Chinese veterinary medicine (TCVM) evaluation and treatment after a diagnosis of oral squamous cell carcinoma of the right rostral mandible. The large tumor (5x4x1 cm) displaced the right mandibular canine tooth, lower incisors and extended ventrally under the tongue. A guarded prognosis had been given by an oncologist with an estimate of 3-6 months survival with palliative care. With the dog’s quality of life the most important consideration, the owner elected to try an integrative medicine approach combining piroxicam, tramadol and TCVM. The TCVM treatment strategies focused on tonifying Spleen Qi, and moving Qi and Blood. Chinese herbal therapy and food therapy were instituted as the central components of the treatment plan. A specifically-designed home-cooked diet was started to tonify Qi, resolve Stagnation and transform Phlegm. Emphasis was placed on food therapy as good nutrition has been shown to increase survival times in humans and animals with cancer. Over a 4-6 month treatment period, clinical signs steadily improved with resolution of the visible oral mass and the pain associated with it. The dog maintained an excellent quality of life for 56 months after the initial diagnosis. This case highlights an excellent clinical outcome for a dog with poor prognosis for long-term survival through an integrative approach using TCVM therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".