¿Cómo mejorar la hidratación y la ingesta hídrica en las personas mayores institucionalizadas? Una revisión de la literatura científica
Bibliographic record
Abstract
BACKGROUND: residents are vulnerable to suffer of dehydration due to physiological changes and the physical and cognitive limitations. AIM: to handle this situation, it has been decided to evaluate the interventions which are carried out for the management of dehydration and low fluid intake in older people living in nursing homes. METHODS: the revised scientific literature review methodology of PRISMA was applied. An electronic database search was performed in PubMed, Scopus, CINAHL and other sources databases. The literature search was carried out between October 2016 and January 2017. Out of a total of 3,379 articles extracted, eleven studies were selected for analysis. In addition, their quality was assessed through Cochrane and the Newcastle-Ottawa Scale. RESULTS: the risk of bias in the studies was mostly medium. Regarding the results, the interventions were classified according to whether they were invasive or non-invasive. Invasive interventions were intravenous and/or subcutaneous fluid therapy. Their effectiveness was related to the clinical improvement of dehydration. However, local reactions appeared. Non-invasive interventions focused on the individualized assistance, the stimulation to drink more and the consideration of the preferences of each resident, producing an increase in fluid intake and an improvement in analytical parameters. CONCLUSIONS: given the peculiarities of the institutionalized elderly population, both types of intervention have been shown to have a positive effect on improving hydration. Nonetheless, non-invasive interventions have confirmed to be more efficient given their simplicity of application and cause fewer adverse effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".