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Record W4308186554

Breed predilections and prognosis for subungual squamous cell carcinoma in dogs.

2022· article· en· W4308186554 on OpenAlexaffabout
Olivia Chiu, Brian Wilcock, Anne Wilcock, A. Michelle Edwards

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBreedMedicineBasal cellOdds ratioBiopsyPathologyInternal medicineBiology
DOInot available

Abstract

fetched live from OpenAlex

Objective: To better document the prevalence, breed predilections, and clinical behavior of subungual squamous cell carcinomas in dogs. Procedure: Retrospective analysis of records from 278 812 canine biopsy submissions including 1518 subungual squamous cell carcinomas from dogs in Canada between the years 2003 and 2021. Results: In agreement with previous studies, giant schnauzers [odds ratio (OR): 56.7], standard schnauzers (OR: 20.3), Gordon setters (OR: 18.3), black standard poodles (OR: 11.1), Kerry blue terriers (OR: 9.4), Rottweilers (OR: 7.0), and several other breeds of large black dogs had a strong predilection for development of subungual squamous cell carcinomas. In giant schnauzers and standard poodles specifically, the risk of developing additional tumors on additional digits was 56%. There were no local postoperative recurrences, and the risk of detecting metastatic disease within 5 y after initial diagnosis was very low at 4%. Conclusion: Moderately large black, or black and tan, dogs have a marked increase in the prevalence of subungual squamous cell carcinomas. At least in giant schnauzers and black standard poodles, the risk of developing additional similar tumors on additional digits is high, but the metastatic risk is very low. Clinical relevance: Veterinarians receiving a histologic diagnosis of subungual squamous cell carcinoma in a large black (or predominantly black) dog should advise the owners of a substantial risk that the dog will develop similar tumors on other digits in 2 or 3 y following initial diagnosis, but that the risk of local recurrence or metastatic spread is extremely low.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.021
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.298
Teacher spread0.245 · 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.

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

Citations5
Published2022
Admission routes2
Has abstractyes

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