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Record W2617157076 · doi:10.1111/hdi.12571

Antithrombotic therapy in end‐stage renal disease

2017· review· en· W2617157076 on OpenAlexvenueno aff
Svetha Chunduri, Jon Folstad, Tushar J. Vachharajani

Bibliographic record

VenueHemodialysis International · 2017
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAntithromboticEnd stage renal diseaseHemodialysisIntensive care medicineStage (stratigraphy)Renal replacement therapyHome hemodialysisInternal medicineDiseaseCardiology

Abstract

fetched live from OpenAlex

The delicate balance of risk vs. benefit of using antiplatelet and antithrombotic agents in the general population is well established. The decision to use these agents in the end stage renal disease (ESRD) population remains complex and difficult. The concomitant association of a prothombotic state with high risk of bleeding in the ESRD population requires individualization and careful clinical judgment before implementing such therapy. There remains a paucity of clinical trials and lack of substantial evidence in literature for safe and effective use of antithrombotic drugs in patients with advanced chronic kidney disease. The current review summarizes the pros and cons of using antiplatelet and antithrombotic agents in primary and secondary prevention of cardiovascular events, evaluate the risks with routine use of anticoagulation for cerebrovascular stroke prevention with nonvalvular atrial fibrillation and role of newer oral anticoagulants as alternate agents in the dialysis population.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.217
GPT teacher head0.446
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2017
Admission routes1
Has abstractyes

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