MétaCan
Menu
Back to cohort
Record W4308181806

Reverse sneezing as a clinical manifestation of nasopharyngeal-oropharyngeal fistula in a dog.

2022· article· en· W4308181806 on OpenAlexaboutno aff
Marianthi Gelatos, Sara A. Colopy, Kenneth R. Waller, Jessica C. Pritchard

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRetchingSoft palateHard palateFistulaTonsillectomySurgeryNasal dischargeVomiting
DOInot available

Abstract

fetched live from OpenAlex

A 6-year-old spayed female Labrador retriever was evaluated for a 3-month history of intermittent reverse sneezing and gagging episodes. Pertinent findings at evaluation included frequent reverse sneezing and non-productive retching. No pathology was visible on sedated oral examination. Contrast-enhanced computed tomography of the skull revealed a gas-filled defect within the left ventral aspect of the soft palate. A non-eroded defect was present in the left caudoventral nasopharyngeal wall on nasopharyngoscopy. Surgical exploration revealed a nasopharyngeal-oropharyngeal fistula within the left palatine tonsillar fossa. The dog had a witnessed oropharyngeal stick injury (OSI) 3 months previous in the location of the fistula. The OSI had been allowed to heal by secondary intention and was treated with an oral antibiotic and NSAID. However, the dog lacked characteristic signs of a chronic OSI such as nasal discharge or abscess formation. The defect in the soft palate was surgically debrided and closed, and the left palatine tonsil was excised. The dog recovered completely with cessation of reverse sneezing and retching episodes.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
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 head0.341
Teacher spread0.290 · 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

Explore more

Same venuePubMedSame topicHead and Neck Surgical OncologyFrench-language works237,207