Bibliographic record
Abstract
There is a growing body of evidence about physiological changes with age that impact fluid and electrolyte balance. It is important that infusion nurses have knowledge in managing care for geriatric patients so they can identify these changes when they are exhibited. Knowing how to minimize the effect of these changes on the health of older adults is critical. The infusion nurse with knowledge of geriatric-focused care can avoid complications and critical illness in older adults. In addition, it is important to provide specific patient education that is grounded in geriatric best practices. This information will assist older adults to better protect themselves from dehydration, kidney injury, and other complications associated with fluid balance, such as delirium. This article reviews the literature on specific changes with aging that predispose older adults to adverse complications with fluid imbalance. New technology in geriatrics that can improve management of fluid status, such as dehydration and electrolyte monitors, are also discussed. This review included searches of the Medline®/PubMed® Database using MeSH terms (National Library of Medicine). Search terms included the following: aging-biological; aging kidney; water-electrolyte imbalance; dehydration; hypo-hypernatremia; hypo-hyperkalemia; delirium; wearable technology; and hydration monitors.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".