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Record W3178180340 · doi:10.1016/j.arr.2021.101408

Haemodynamic frailty – A risk factor for acute kidney injury in the elderly

2021· review· en· W3178180340 on OpenAlexafffund
Neil G. Docherty, Christian Delles, Patrick C. D’Haese, Anita T. Layton, Carlos Martínez‐Salgado, Benjamin A. Vervaet, Francisco J. López‐Hernández

Bibliographic record

VenueAgeing Research Reviews · 2021
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Waterloo
FundersInstituto de Salud Carlos IIINatural Sciences and Engineering Research Council of CanadaMinisterio de Ciencia e InnovaciónBritish Heart Foundation
KeywordsMedicineAcute kidney injuryIntensive care medicineContext (archaeology)HemodynamicsIntravascular volume statusKidney diseaseCardiologyRisk factorInternal medicine

Abstract

fetched live from OpenAlex

Clinical frailty in the elderly is defined by a composite measure of functional psychomotor decline. Herein, we develop the concept of haemodynamic frailty (HDF), a state of increased predisposition to disease prevalent in the elderly and characterised by impairment of the network of compensatory responses governing the defence of circulatory volume and adaptive haemodynamic function. We review the factors predisposing the elderly to HDF, with a focus on the impaired capacity to sustain total body water balance. As a component of HDF, dehydration generates vulnerability to diseases caused by tissue hypoperfusion, including acute kidney injury. We provide a detailed mechanistic explanation of how dehydration and depletion of the intravascular volume impacts on renal blood flow to become an important element of the heightened risk of acute kidney injury (AKI) in the elderly. We bring these mechanistic considerations into the clinical context with reference to examples of how pre-renal (haemodynamic) and intrinsic (involving renal parenchymal damage) AKI risk is elevated in the setting of dehydration. Finally, we present HDF as a state of opportunity to prevent disease, for which diagnostic and interventional standards need to be refined. Further prospective studies are warranted to help clarify the clinical utility of assessing and managing HDF with regard to the mitigation of AKI risk in the elderly.

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)0.008
metaresearch head score (Gemma)0.006
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 score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.205
GPT teacher head0.499
Teacher spread0.294 · 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

Citations30
Published2021
Admission routes2
Has abstractyes

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