Haemodynamic frailty – A risk factor for acute kidney injury in the elderly
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".