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Record W2967233321 · doi:10.1007/s00134-019-05707-w

Mechanisms for hemodynamic instability related to renal replacement therapy: a narrative review

2019· review· en· W2967233321 on OpenAlexafffund
Adrianna Douvris, Khalid Zeid, Swapnil Hiremath, Sean M. Bagshaw, Ron Wald, William Beaubien‐Souligny, Jennifer Kong, Claudio Ronco, Edward G. Clark

Bibliographic record

VenueIntensive Care Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Michael's HospitalUniversity of AlbertaUniversity of TorontoUniversity Health NetworkOttawa HospitalUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsIntensive care medicineMedicineRenal replacement therapyHemofiltrationKidney diseaseContext (archaeology)HemodialysisModalitiesPsychological interventionInternal medicine

Abstract

fetched live from OpenAlex

Hemodynamic instability related to renal replacement therapy (HIRRT) is a frequent complication of all renal replacement therapy (RRT) modalities commonly used in the intensive care unit. HIRRT is associated with increased mortality and may impair kidney recovery. Our current understanding of the physiologic basis for HIRRT comes primarily from studies of end-stage kidney disease patients on maintenance hemodialysis in whom HIRRT is referred to as 'intradialytic hypotension'. Nonetheless, there are many studies that provide additional insights into the underlying mechanisms for HIRRT specifically in critically ill patients. In particular, recent evidence challenges the notion that HIRRT is almost entirely related to excessive ultrafiltration. Although excessive ultrafiltration is a key mechanism, multiple other RRT-related mechanisms may precipitate HIRRT and this could have implications for how HIRRT should be managed (e.g., the appropriate response might not always be to reduce ultrafiltration, particularly in the context of significant fluid overload). This review briefly summarizes the incidence and adverse effects of HIRRT and reviews what is currently known regarding the mechanisms underpinning it. This includes consideration of the evidence that exists for various RRT-related interventions to prevent or limit HIRRT. An enhanced understanding of the mechanisms that underlie HIRRT, beyond just excessive ultrafiltration, may lead to more effective RRT-related interventions to mitigate its occurrence and consequences.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.380
Teacher spread0.331 · 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.

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

Citations180
Published2019
Admission routes2
Has abstractyes

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