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Record W4389836927

Highlighting the Benefits of AlpaWash in Wound Care: Case Reports in Small Animals.

2024· article· en· W4389836927 on OpenAlexaff
Flávia Semighini, Laís de Azevedo Fornaciari, Vanessa Pinheiro, Rodrigo Lupatini, Halema Haiub, Erica Cull, Raman Sidhu

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsCégep de Saint-Laurent
Fundersnot available
KeywordsMedicineAnamnesisWound careCATSVeterinary medicineLesionIntensive care medicineSurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Several studies have reported the potential of utilizing natural extracts in wound care, emphasizing those with anti-inflammatory and antimicrobial properties. In veterinary medicine, dermal-lesion treatment can be very challenging considering the patient's compliance and awareness of their condition. In this article, six veterinary case reports have been presented to elucidate the advantages of AlpaWash, a topical application utilized in combination with the prescribed medications of the patients, for the purpose of addressing the process of wound healing in three cats and three dogs. All animals were admitted to the veterinary clinic and treated under the supervision of a veterinarian. The cats and dogs were rescued from streets by people who lived in the neighborhood of Cão Bento´s Veterinary. They were admitted for the purpose of receiving medical care due to recent minor injuries or wounds due to a pet fight, preexisting condition, or accident. A veterinarian performed the anamnesis and monitored the animals during the period of treatment with AlpaWash. In each case report, the veterinarian observed significant improvement in the wound closures, and lesions healed within a couple of weeks to a couple of months depending on the case. The outcomes demonstrate the benefits of AlpaWash topical application and suggest that AlpaWash may be an alternative vehicle for compounded preparations in wound management.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.194
Teacher spread0.167 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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