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Record W3081389091 · doi:10.1111/vco.12645

Clinical outcome in 23 dogs with exocrine pancreatic carcinoma

2020· article· en· W3081389091 on OpenAlexaff
Christopher J. Pinard, Samuel E. Hocker, Kristen Weishaar

Bibliographic record

VenueVeterinary and Comparative Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineLethargyVomitingAnorexiaDiseaseGastroenterologyInternal medicineRetrospective cohort studyCarcinomaMetastasisExocrine pancreatic insufficiencyRadiation therapyLymph nodeChemotherapyOncologyPancreasCancer

Abstract

fetched live from OpenAlex

Exocrine pancreatic carcinoma is uncommon in the dog and the veterinary literature surrounding the disease is minimal. Twenty-three cases of canine exocrine pancreatic carcinoma were reviewed in a retrospective manner to obtain information on clinical presentation, behaviour and survival associated with the disease. Presenting clinical signs were nonspecific and included anorexia, lethargy, vomiting and abdominal pain. The overall median survival time was only 1 day but was confounded by the large number of dogs that were euthanized shortly after diagnosis. Metastatic disease was detected in 78% of cases at the time of diagnosis, attesting to the aggressive nature of the disease. Neither lymph node metastasis, tumour size nor tumour location had an impact on overall survival. Only one patient was a previous diabetic who is contrary to reports of the disease in people and felines. This retrospective study reaffirms the need for early detection measures to optimize disease control. However, the benefits of therapy with surgery or radiation and adjuvant chemotherapy remain to be elucidated in dogs with exocrine pancreatic carcinoma.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.345
GPT teacher head0.482
Teacher spread0.137 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2020
Admission routes1
Has abstractyes

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