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Record W3048272763 · doi:10.1111/hdi.12867

Continuous venovenous hemodialysis may be effective in digoxin removal in digoxin toxicity: A case report

2020· article· en· W3048272763 on OpenAlexvenueno aff
Cenk Gökalp, Aysun Doğan, İlhan Kurultak, Sedat Üstündağ

Bibliographic record

VenueHemodialysis International · 2020
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsDigoxinMedicineHemodialysisHeart failureDialysisHyperkalemiaToxicityHypervolemiaAtrial fibrillationCardiologyInternal medicineAntidotePeritoneal dialysisAnesthesiaBlood volume

Abstract

fetched live from OpenAlex

Digoxin is a cardiac glycoside that is used for the treatment of heart failure and atrial fibrillation. Besides its careful close follow-up, toxicity affects nearly 1% of congestive heart failure patients. Cessation of the drug, appropriate electrolyte and rhythm control and digoxin-Fab antibody are the mainstay for toxicity treatment in these patients. As known, hemodialysis and peritoneal dialysis are not effective by the means of digoxin removal. We present a 66-year-old patient who admitted to hospital with digoxin toxicity and severe acute kidney injury. The patient was treated with continuous venovenous hemodialysis because of her hypervolemia, hyperkalemia, cardiac instability, and the thought of probable decrease in digoxin levels concerning the continuous nature of solute clearance. Without the treatment using digoxin-specific Fab antibodies, the patient's digoxin level was decreased successfully with continuous venovenous hemodialysis. In conclusion, continuous venovenous hemodialysis may be a treatment option in digoxin toxicity especially those who suffer from severe renal dysfunction and cannot access digoxin antidote.

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)0.002
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 score0.006
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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