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Clinical, histological and immunohistochemical study of feline viral plaques and bowenoid <i>in situ</i> carcinomas

2006· article· en· W2084758249 on OpenAlexaff
Sylvia Wilhelm, F. Degorce‐Rubiales, Dale L. Godson, Claude Favrot

Bibliographic record

VenueVeterinary Dermatology · 2006
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCATSPapillomaviridaeAntigenHuman papillomavirusBiologyVirologyCancerImmunologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Feline viral plaques (FVP) induced by papillomavirus (PV) are often hyperpigmented and flat warts. The fact that up to 47% of bowenoid in situ carcinomas (BISC), which also usually occur in the form of hyperpigmented plaques, are positive for PV antigen in immunochemistry suggests that BISC could evolve from FVP. The relationship between the presence of PV antigens and the clinical and histological features of 26 cases of feline dermatoses (clinically described as pigmented plaques and with histological diagnosis of FVP and/or BISC) was therefore determined. The cases were classified into one of the three following groups: FVP, FVP + BISC or BISC. Immunohistological detection of papillomavirus group-specific antigen was performed using a polyclonal rabbit antibovine papillomavirus antiserum. Of the seven cases in the FVP group, six were deemed positive by immunohistology as were all 10 cats in the FVP + BISC group. On the other hand, only one of the nine BISC cats was positive. The presence of both FVP and BISC lesions in some cats and the high detection rate of PV antigens in the FVP and FVP + BISC groups suggest that both conditions might have the same viral cause and that some BISC may evolve from FVP. The low rate of viral antigen detection in the BISC group indicates another cause or a loss of viral replication during the cancerogenesis.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.051
GPT teacher head0.379
Teacher spread0.328 · 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

Citations60
Published2006
Admission routes1
Has abstractyes

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