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Record W4391051690 · doi:10.31949/agrivet.v11i2.7904

Laporan Kasus: Pyometra Pada Anjing Ras Labrador

2023· article· en· W4391051690 on OpenAlexaboutno aff
Anna Zukiaturrahmah, Juliadi Ramadhan, Ario Ridho Gelagar, Usma Aulia

Bibliographic record

VenueAgrivet Jurnal Ilmu-Ilmu Pertanian dan Peternakan (Journal of Agricultural Sciences and Veteriner) · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsnot available
Fundersnot available
KeywordsPyometraLeukocytosisNeutrophiliaMedicinePhysical examinationUterusGross examinationInternal medicineGastroenterologySurgeryPathology

Abstract

fetched live from OpenAlex

Pyometra is one of the important causes of infertility in female animals, including dogs. Pyometra is accumulation of pus in the uterus caused by bacteria that are normally in the uterus but in certain circumstances become pathogens due to hormonal influences. This case report aims to determine the etiology, clinical symptoms, diagnosis, prognosis and treatment of pyometra disease in dogs. The examination method that is carried out is physical examination and investigations, hematology, blood chemistry, and ultrasound. Based on a physical examination in the form of inspection of the lesions on the vulva and abdominal enlargement. Palpation of the mesogastrium and hypogastrium is the presence of abdominal tension. Hematologic results show leukocytosis, neutrophilia, and hyperchromic normocytic anemia. Blood chemistry examination results showed azotemia, increased AST, ALP and GGT. Ultrasound examination results show that an anechoic period is accumulation of uterine fluid and thickening of the uterine wall. The diagnosis of this case is pyometra. The therapy performed is ovariohisterectomy surgery. After surgery animals were given medicines including Cephalexine, Meloxicam, Ursodeoxycholic acid, and S-adenosylmethionine (SAMe). On the sixth day the animal surgery post improves again and is allowed to be taken home.

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 score0.892
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.035
GPT teacher head0.244
Teacher spread0.210 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAgrivet Jurnal Ilmu-Ilmu Pertanian dan Peternakan (Journal of Agricultural Sciences and Veteriner)Same topicAquatic life and conservationFrench-language works237,207