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Record W4388111156 · doi:10.30954/2277-940x.03.2023.25

Acute Kidney Disease in Dogs an Epidemiological Study

2023· article· en· W4388111156 on OpenAlexaboutno aff
Dawar Pooja

Bibliographic record

VenueJournal of animal research · 2023
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyMedicineDiseaseVeterinary medicinePathologyIntensive care medicine

Abstract

fetched live from OpenAlex

The present study was aimed to record the occurrence of acute kidney diseases in dogs.Overall occurrence of acute kidney diseases was 01.18 % (32/2696).Out of 148 suspected dogs 21.62% (32/148) suffered with acute kidney disease.The age wise occurrence of acute kidney disease was found to be higher in dogs aged between 4-8 years 28.12% (18/64), followed by dogs above 8 years 25.00% (12/48) of age and was less in dogs 1-4 years age 10.52% (02/19).Breed wise occurrence was higher in Labrador retriever 26.82% (11/41). HIGHLIGHTSm Overall occurrence of acute kidney diseases was 01.18 %. m The age wise occurrence of acute kidney disease was found to be higher in dogs aged between 4-8 years of age.

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)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.353
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.507
GPT teacher head0.561
Teacher spread0.054 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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