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Management of exuberant granulation tissue in horses: Review of 18 cases

2025· article· en· W4415137471 on OpenAlexaff
Brihaspati Bharti, Satyaveer Singh, Shailendra Singh, Balendra Pratap Singh

Bibliographic record

VenueIndian Journal of Veterinary Surgery · 2025
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsResverlogix (Canada)
Fundersnot available
KeywordsGranulation tissueCauterizationBandageHistopathologyHyperaemiaHorseDebridement (dental)Necrotic tissue

Abstract

fetched live from OpenAlex

The retrospective study included 18 horses with the history of exuberant granulation tissue. Analysis of the condition was done based on age and sex of the animal, affected anatomical site, treatment and recovery status. Highest incidence of exuberant granulation tissue was noticed on limbs (n=12) followed by thorax, abdomen, pelvis (n=4) and head and neck (n=2). Mean age of the affected horses was 5.05 yr; 14 were males and 4 females. Surgical excision was done in most of the horses followed by firing, cauterization with potassium permanganate crystals and application of corticosteroid ointment. Postoperative medication included administration of antibiotic and anti-inflammatory drugs and oral serratopeptidase, along with application of pressure bandage in most of the cases. All the horses recovered uneventfully in about 10-90 days. Histopathological examination confirmed the granulation tissue, characterized by the proliferation of fibrous tissue along with blood vessels.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.131
GPT teacher head0.429
Teacher spread0.298 · 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 designSystematic review
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
Published2025
Admission routes1
Has abstractyes

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