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Record W2619217939 · doi:10.1111/hdi.12550

Safety of arteriovenous fistulae and grafts for continuous renal replacement therapy: The Michigan experience

2017· article· en· W2619217939 on OpenAlexvenueno aff
Anas Al Rifai, Nidhi Sukul, Robert Wonnacott, Michael Heung

Bibliographic record

VenueHemodialysis International · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsnot available
FundersAmerican Society of Nephrology
KeywordsMedicineRenal replacement therapyArteriovenous fistulaHemodialysisSurgeryCatheterComplicationDialysis catheterSingle CenterThrombosisEnd stage renal diseaseFistulaDialysis

Abstract

fetched live from OpenAlex

INTRODUCTION: Arteriovenous fistula or graft (AVF/AVG) use is widely considered contraindicated for continuous renal replacement therapy (CRRT), yet insertion of hemodialysis (HD) catheters can carry high complication risk in critically ill end-stage renal disease (ESRD) patients. METHODS: Single-center analysis of 48 consecutive hospitalized ESRD patients on maintenance HD who underwent CRRT using AVF/AVG from 2012 to 2013. Primary outcome was access-related complications. FINDINGS: Mean age was 60 years, 48% were male, and 88% required vasopressor support. Median duration of AVF/AVG use for CRRT was 4 days (range 1-34). Ten (21%) patients had access complications (5 bleeding, 5 infiltration, 1 thrombosis); 5 (10.4%) required catheter placement. Overall 31 (65%) patients survived to hospital discharge and AVF/AVG access was functional at the time of discharge in 29 (94%) patients. DISCUSSION: In our experience, use of AVF/AVG for CRRT can be performed with a low serious complication rate and low risk of access loss, potentially avoiding catheter-related complications.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.364
Teacher spread0.322 · 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 designObservational
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

Citations15
Published2017
Admission routes1
Has abstractyes

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