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Generalities in canine papillomavirus: systematic review of case reports

2023· article· en· W4382196387 on OpenAlexaboutno aff
Guillermo Cano‐Verdugo, Gabriela Guadalupe Verdugo-Lizárraga, David Emmanuel Gámez-Sánchez

Bibliographic record

VenueVeterinaria México OA · 2023
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsBasal cellMedicineSystematic reviewMedical literaturePathologyMEDLINEBiology

Abstract

fetched live from OpenAlex

Canine papillomavirus (CPV) is a common entity in dogs that can be transmitted by direct and indirect contact and cause lesions in various parts of the body. It is the main cause of benign tumors; however, if not detected in time, it is a risk factor for the development of squamous cell carcinoma, documented with high mortality. To clarify demographic generalities, location of lesions, and findings involved in CPV detection, a systematic review of case reports of CPV was performed. The PRISMA statement was followed. Literature was searched in PubMed, DOAJ, and CAB Abstracts from 2011 to date. The articles collected were tabulated in Excel with the variables of interest. A total of 54 articles were obtained from the search, and 11 were included in the review after the screening and selection process. The analysis of the information allowed us to identify that among the case reports there were 4 investigations with male dogs, 2 females and 5 unspecified. Age ranged from 2 to 12 years. The breed with more cases reported was the Labrador retriever and 6 reported cases with neutered patients. Regarding the location of the lesions, the most common was the oral cavity, and the main findings were the need to identify new subtypes of CPV, and the development of lesions at lower CD4 and CD8 lymphocyte counts. Further research, encouragement of veterinary medical personnel, and dissemination of CPVrelated literature are needed to make this pathology visible and initiate future public health actions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.080
GPT teacher head0.385
Teacher spread0.304 · 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 designCase report
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

Citations0
Published2023
Admission routes1
Has abstractyes

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