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Record W3033189473 · doi:10.1155/2020/9716179

Nasopharyngeal Vascular Hamartoma in a Dog

2020· article· en· W3033189473 on OpenAlexaboutno aff
Annalisa N. Judy, Alexander I. Krebs, Joseph Haynes, Nina R. Kieves

Bibliographic record

VenueCase Reports in Veterinary Medicine · 2020
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLabrador RetrieverHistopathologyStridorHamartomaEtiologySurgeryBiopsyRadiologyPathology

Abstract

fetched live from OpenAlex

An 8-year-old spayed female 32 kg Labrador retriever was presented for further investigation into the underlying cause of dyspnea, stertor, and sleep apnea present for three months and worsening over 30 days. There were significant reduction in airflow through the nares and loud inspiratory stridor. Thoracic and cervical radiographs made were normal. A skull CT and retrograde rhinoscopy showed a mass occluding the majority of the nasopharynx above the caudal third of the hard palate. The main differential diagnoses included a neoplastic mass vs. inflammatory mass vs. cyst vs. mucous obstruction. There was no destruction of nasal turbinates, making a benign etiology more likely. Biopsy of the mass showed an inflammatory process. En bloc excision of the mass was performed via ventral rhinotomy without complication. Histopathology of the excised mass revealed it to be a mucosal vascular hamartoma. The dog recovered uneventfully and had no further respiratory issues, short or long term. Although vascular hamartomas are a rare finding in veterinary medicine, they can be found in a wide variety of species and anatomic locations. They should be considered when naming differentials for benign mass lesions throughout the body, including the nasopharynx. Although they are benign masses in nature, they can be clinically significant and should be addressed. Prognosis after removal in this location is excellent.

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)0.000
metaresearch head score (Gemma)0.000
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.327
Teacher spread0.271 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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