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Record W2165330963 · doi:10.2460/javma.239.9.1232

Diagnosis and treatment of truncal cutaneous pythiosis in a dog

2011· article· en· W2165330963 on OpenAlexaff
Kelley M. Thieman, Kristin A. Kirkby, Alison Flynn-Lurie, Amy M. Grooters, Nicholas J. Bacon

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2011
Typearticle
Languageen
FieldVeterinary
TopicInfectious Diseases and Mycology
Canadian institutionsMerck Canada Inc. (Canada)
Fundersnot available
KeywordsMedicinePhysical examinationSurgeryDorsumAbdomenSoft tissueSurgical excisionHistopathological examinationItraconazoleLymph nodeRadiologyAnatomyDermatologyPathologyAntifungal

Abstract

fetched live from OpenAlex

CASE DESCRIPTION: A 4-year-old spayed female Boxer was evaluated for a cutaneous mass located on the dorsum. The mass had been present for 6 weeks and was increasing in size. CLINICAL FINDINGS: A mass of approximately 10 cm in diameter was detected on the dorsum cranial to the right ilial wing. Histologic examination of a tissue sample from the mass led to the diagnosis of cutaneous pythiosis. Computed tomography of the abdomen and the mass were performed and revealed a contrast-enhancing soft tissue mass of the dorsum and enlarged intra-abdominal lymph nodes. TREATMENT AND OUTCOME: The dog underwent surgical excision of the cutaneous mass, including 5-cm skin margins and deep margins of 2 fascial planes. The mass was completely excised on the basis of results of histologic examination of surgical margins. The dog received itraconazole and terbinafine by mouth for 3 months following surgery. Recheck examination at 20 months postoperatively showed no signs of recurrence of pythiosis at the surgical site. CLINICAL RELEVANCE: Aggressive surgical excision in combination with medical treatment resulted in a favorable long-term (> 1 year) outcome in this dog. Thorough workup including diagnostic imaging and lymph node evaluation is recommended. If surgery is to be performed, skin margins of 5 cm and deep margins of 2 fascial planes are recommended.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.048
GPT teacher head0.326
Teacher spread0.277 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

Explore more

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