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Record W2982261907 · doi:10.35508/jkv.v6i2.932

Case Report: Penanganan Obstruksi Esofagus Pada Anjing Labrador Retrievers

2019· article· en· W2982261907 on OpenAlexaboutno aff
Tri Utami, Tarsisius Considus Tophianong

Bibliographic record

VenueJurnal Kajian Veteriner · 2019
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtropineEsophagusPremedicationForeign bodyLabrador RetrieverPhysical examinationXylazineAnesthesiaSurgeryKetamine

Abstract

fetched live from OpenAlex

Esophageal obstruction is a condition that is commonly experienced by dogs and causes disruption to the mobilization of food and water to the stomatch. The aim of this study is to provide information on the management of esophageal obstruction in a dog based on the type and location of the obstruction. The material used in this case study is a Labrador retriever dog, female, 2 years old, brownish red and weighing 24 kg. Based on history, clinical symptoms and radiographic examination, the diagnosis of this case was obstruction of a foreign body in intraluminal esophagus. Before treatment, the dog was anesthetized by premedication Atropine sulfate at 0.02 mg/kg body weight sub-cutaneously, and induction of anesthesia through combination injection of Ketamin HCL dose 10 mg/kg body weight and Xylazine dose 2 mg/ kg body weight intramuscularly. Goal treatment that has been done in this case is taking a foreign body in the form of a piece cow bone in intraluminal esophagus through the oral cavity.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.290
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 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
Published2019
Admission routes1
Has abstractyes

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