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Record W4292064409 · doi:10.21708/avb.2022.16.3.10837

Digoxin for atrial fibrillation: good, but not too good: a case report

2022· article· en· W4292064409 on OpenAlexaboutno aff
Gustavo Luiz Gouvêa de Almeida, Marcelo Barbosa de Almeida, Ana Carolina Mendes dos Santos, Ângela Vargas, Sophie Ballot, Elaine Waite de Souza

Bibliographic record

VenueActa Veterinaria Brasilica · 2022
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsDigoxinMedicineAtrial fibrillationDigitoxinHeart failureDigitalisInternal medicineCardiologyAmiodaroneFurosemideAnesthesia

Abstract

fetched live from OpenAlex

Digoxin is a cardiotonic glycoside that is traditionally used for the treatment of heart failure and atrial fibrillation in humans and animals. However, the use of digoxin is still a challenge in clinical practice due to its narrow therapeutic range and its potential interaction with several drugs, which could facilitate the development of toxicity. A 12-year-old Labrador retriever was referred with a clinical diagnosis of heart failure and atrial fibrillation, anorexia, vomiting, and diarrhea. He had been medicated with digoxin, furosemide, lisinopril, and amiodarone. The patient also showed clinical signs of hip osteoarthritis and received firocoxib for four days. He additionally received drugs for gastrointeritis. The electrocardiogram demonstrated atrial fibrillation and signs of digitalis toxicity. Laboratory examination showed a high concentration of plasma digoxin, and 5 days after withdrawal of the drugs, the symptoms disappeared, as did the digitalis effects seen in the previous electrocardiogram.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.010
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0040.002
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0100.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.087
GPT teacher head0.354
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

Citations2
Published2022
Admission routes1
Has abstractyes

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