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Record W4383000822 · doi:10.59565/iunl5235

Traditional Chinese Veterinary Medicine to Treat Oral Cancer in a Labrador Retriever

2022· article· en· W4383000822 on OpenAlexaboutno aff
Cynthia Redfield

Bibliographic record

VenueAmerican Journal of Traditional Chinese Veterinary Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)CancerPalliative careTraditional Chinese medicineVeterinary medicineInternal medicineAlternative medicinePathologyNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.102
GPT teacher head0.396
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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