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Record W2904500270 · doi:10.1155/2018/7381505

Anticoagulant Related Nephropathy Induced by Dabigatran

2018· article· en· W2904500270 on OpenAlexaff
Nazia Sharfuddin, Mahra Nourbakhsh, Alan Box, Hallgrímur Benediktsson, Daniel A. Muruve

Bibliographic record

VenueCase Reports in Nephrology · 2018
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDabigatranMedicineNephropathyAcute kidney injuryWarfarinRenal biopsyRenal functionKidney diseaseInternal medicineUrinalysisAtrial fibrillationGastroenterologyUrologyEndocrinologyDiabetes mellitusUrine

Abstract

fetched live from OpenAlex

We describe a case of biopsy-proven dabigatran related nephropathy in a patient without underlying IgA nephropathy. To date, dabigatran related nephropathy was only reported in patients with concurrent or undiagnosed IgA nephropathy, suggesting that it may predispose patients to dabigatran associated injury. The patient is an 81-year-old woman with multiple medical comorbidities, including nonvalvular atrial fibrillation, who was anticoagulated with dabigatran. She presented to hospital with acute kidney injury in the setting of volume overload. Her estimated glomerular filtration rate decreased from a baseline of 57 mL/min/1.73 m 2 to 4 mL/min/1.73 m 2 , necessitating hemodialysis. Renal ultrasound findings, fractional excretion of sodium, and urinalysis suggested acute kidney injury. Renal biopsy showed acute tubular injury, tubular red blood cell casts, and an absence of active glomerulonephritis, similar to the pathological findings of warfarin related nephropathy. A diagnosis of anticoagulant related nephropathy secondary to dabigatran was therefore established. This case demonstrates that dabigatran, like warfarin, may increase tubular bleeding risk in patients, irrespective of underlying kidney or glomerular 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.348
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.045
GPT teacher head0.332
Teacher spread0.288 · 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 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

Citations14
Published2018
Admission routes1
Has abstractyes

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