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Record W1979766080 · doi:10.2460/javma.233.6.945

Persistent vaginal hemorrhage caused by vaginal vascular ectasia in a dog

2008· article· en· W1979766080 on OpenAlexaboutno aff
Jessica A. Gower, Sandra J. Schoeniger, Susan Gregory

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2008
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEctasiaSurgery

Abstract

fetched live from OpenAlex

CASE DESCRIPTION: A 6-year-old 36.5-kg (80.3-lb) spayed female Labrador Retriever was evaluated because of an 11-month history of vaginal bleeding. Previous radiographic, endoscopic, and surgical interventions had failed to detect an underlying cause for the bleeding. The dog was examined on an emergency basis because of severe anemia after the bleeding increased in severity. CLINICAL FINDINGS: Bleeding was severe, and results of vaginoscopy and radiography (after administration of a contrast agent) did not confirm the cause of the hemorrhage. An exploratory episiotomy revealed multiple bleeding vascular abnormalities within the vaginal mucosa cranial to the external urethral orifice, which were suggestive of vascular ectasia. TREATMENT AND OUTCOME: A total vaginectomy was performed via a ventral midline incision and a pubic symphysiotomy. Macroscopic and microscopic examination of excised vaginal tissues confirmed changes compatible with vascular ectasia. The dog made an uneventful recovery with no further vulval bleeding until 19 months after surgery, at which time vulval bleeding recurred. Further investigation and treatment were declined by the owner. CLINICAL RELEVANCE: Vascular ectasia may be a cause of chronic vaginal hemorrhage and life-threatening anemia in dogs. In the dog of this report, the diagnosis was made on the basis of direct observation during exploratory episiotomy and histopathologic findings. To manage the condition, total vaginectomy was performed; however, despite radical surgery, bleeding recurred.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.313
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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 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

Citations4
Published2008
Admission routes1
Has abstractyes

Explore more

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