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Record W2096839638 · doi:10.5455/vetworld.2012.285-287

Analysis of Serum Ascites Albumin Gradient Test in Ascitic Dogs

2012· article· it· W2096839638 on OpenAlexaboutno aff
M. Saravanan, Kamalesh Kumar Sharma, MRavi Kumar, H Vijaykumar, D. B. Mondal

Bibliographic record

VenueVeterinary World · 2012
Typearticle
Languageit
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsAscitesAlbuminAscitic fluidInternal medicineGastroenterologyMedicineSerum albumin

Abstract

fetched live from OpenAlex

The aim of the study was to evaluate Serum Ascites Albumin Gradient (SAAG) in ascitic dogs.Study was conducted at Referral Veterinary Poly Clinic, Indian Veterinary Research Institute, Izatnagar.Sick dogs were brought with clinical signs suggestive of distended abdomen and inappetance.General clinical examination, biochemical, ultrasound examination, abdominocentesis and peritoneal fluid examination were performed.Spitz dogs had more incidences of ascites followed by Labrador Retrievers.Male dogs had more incidence than female dogs and most ascites were noticed in 4-5 years aged dogs.Mean ± SE of serum ascites albumin gradients (SAAG) are 1.793 ± 0.185.SAAG can be used as a screening test in ascetic due to chronic liver disease.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

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 head0.033
GPT teacher head0.297
Teacher spread0.264 · 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 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

Citations7
Published2012
Admission routes1
Has abstractyes

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