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Record W3157528455 · doi:10.17695/rcsnevol19n1p69-74

INSUFICIÊNCIA RENAL CRÔNICA EM LABRADOR ASSOCIADA AO USO DE ANTI-INFLAMATÓRIOS – RELATO DE CASO

2021· article· pt· W3157528455 on OpenAlexaboutno aff
Nadja Soares Vila Nova, Renata Celis dos Santos Chagas, Francisca Manuela De Sousa Freire, Sthefany Kristinne Alves de Melo, Marcos Wanderson Vieira Monteiro

Bibliographic record

VenueRevista de Ciências da Saúde Nova Esperança · 2021
Typearticle
Languagept
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsAzotemiaMedicineGynecologyInternal medicineRenal function

Abstract

fetched live from OpenAlex

O diagnóstico da Insuficiência Renal Crônica (IRC) inclui a identificação de importantes quadros como azotemia, ou outras alterações que afetem o funcionamento da filtração renal, alterando, assim, a homeostase. Um cão Labrador, macho, 13 anos, 28 kg foi atendido em uma Clínica Veterinária de Natal, Rio Grande do Norte. Na anamnese foi relatado que o animal tinha dores articulares crônicas, atrofia dos membros torácicos e histórico de descompressão de vértebras lombares e artrodese do joelho direito, além de fazer uso recorrente de anti-inflamatórios (meloxicam) por parte dos tutores. Foram solicitados hemograma, bioquímicos séricos e ultrassonografia de abdômen e estes indicaram quadro grave de azotemia (Ureia: 293, 85 mg/dL e Creatinina: 5,78 mg/dL) e imagem compatível com bexiga neurogênica. Administrou-se fluido Ringer com Lactato, Tramadol, Dipirona, Hidróxido de Alumínio e Ranitidina. Após 7 dias de internação, o animal apresentava dor ao andar e urinar, dificuldade no esvaziamento da bexiga, não se alimentava nem bebia água. Devido à persistência da azotemia (Ureia: 148,39 mg/dL e Creatinina: 5,03 mg/dL) o animal foi eutanasiado. O uso de anti-inflamatórios ao longo dos anos levou à insuficiência renal crônica.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.323
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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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