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Record W4220852570 · doi:10.1002/vrc2.349

Maxillary osteolysis as a result of trigeminal nerve sheath tumour in a dog

2022· article· en· W4220852570 on OpenAlexaboutno aff
Viktoriya Indzhova, Martı́ Pumarola, Barbara Jardim Gomes, Mark Gosling, Josep Brocal

Bibliographic record

VenueVeterinary Record Case Reports · 2022
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTrigeminal nerveOsteolysisAnatomyMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract A 2.5‐year‐old, female, entire Labrador retriever presented with right‐sided head tilt, vestibular ataxia and right‐sided masticatory muscle atrophy. The most likely neuroanatomical localisation was the right‐sided brainstem involving the vestibular system and also the trigeminal nerve. Magnetic resonance imaging and computed tomography revealed enlargement of the right trigeminal nerve and osteolysis of the corresponding innervated maxillary bone. Histopathological analysis confirmed a malignant nerve sheath tumour of the trigeminal nerve that adhered, infiltrated, invaded and fragmented the bone. This is the first report and imaging description of maxillary osteolysis secondary to nerve sheath tumour in a dog.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.300
Teacher spread0.270 · 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 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
Published2022
Admission routes1
Has abstractyes

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