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Record W4410131511 · doi:10.1177/01926233251335320

Bilateral Basal Nuclei Vacuolar Lesions: A Novel and Emerging Potential Background Finding in Beagle Dogs

2025· article· en· W4410131511 on OpenAlexaff
Stefanie Arms, Stephen D. Cahalan, Félix Goulet, Katja Hempel, Giovanni Pellegrini, Sophie Rau, Julia Schlichtiger, Thomas Nolte

Bibliographic record

VenueToxicologic Pathology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotoxicology and immune responses
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsBeagleLesionBasal (medicine)PathologyMedicineTerminologyInternal medicineLinguistics

Abstract

fetched live from OpenAlex

In this case presentation, the speaker and co-authors represented a group of scientists engaged in a cross-institutional precompetitive working group focused on elucidating a novel background change in the basal nuclei of Beagle dogs. The group's ongoing efforts since first publication of the lesion in 2024 enabled further characterization of the lesion and revealed additional incidences in control animals. The characterization, including newly discovered lesion features, and terminology of the condition were outlined and suggestions for interpretation in nonclinical toxicity studies were given.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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