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Hereditary nasal parakeratosis in Labrador Retrievers

2003· article· en· W1973008943 on OpenAlexafffundabout
Nadia Pagé, Manon Paradis, Jean‐Martin Lapointe, Robert W. Dunstan

Bibliographic record

VenueVeterinary Dermatology · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversité de Montréal
FundersAcadémie de Médecine Vétérinaire du Québec
KeywordsParakeratosisStratum spinosumPathologyStratum corneumAcanthosisDyskeratosisDesquamationLymphoplasmacytic LymphomaMedicineHyperkeratosis

Abstract

fetched live from OpenAlex

Hereditary nasal dermatitis is reported in 14 Labrador Retrievers and 4 Labrador Retriever crosses. This appears to be a newly described inherited disorder for which an autosomal recessive mode of inheritance is suspected. The lesions were first noted between 6 and 12 months of age. Histopathological analysis revealed parakeratotic hyperkeratosis, often with marked multifocal accumulation of proteinaceous fluid between keratinocytes within the stratum corneum and superficial stratum spinosum. There was also a sub-basal lymphoplasmacytic infiltration within the superficial dermis. Immunohistochemistry staining for IgG (n = 4), distemper and papillomaviruses (n = 4) were negative, as were serum antinuclear antibody serology (n = 4) and fungal culture (n = 7). Electron microscopy revealed an altered cornification process: retention of nuclear chromatin, absence of lamellar bodies and marked intercellular oedema. Dogs did not respond to oral administration of zinc methionin (n = 3), cephalexin (n = 4), vitamin A alcohol (n = 1) or topical tretinoin (n = 1). Improvement of the lesions was obtained with topical vitamin E (n = 2), petroleum jelly (n = 2), and propylene glycol (n = 5).

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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.276
Teacher spread0.257 · 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
Published2003
Admission routes3
Has abstractyes

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