Bibliographic record
Abstract
Disability in older adults has become a significant burden, both individually and socially, due to the rapidly aging population in Korea. It is important to manage both frailty and chronic diseases to delay disability. Frailty, which is considered to be a transition phase between healthy status and disability, is defined as a significant decline in functional reserves of multiple organ systems and the resultant extreme vulnerability to stressors, leading to a higher risk of adverse health-related outcomes. The frailty phenotype and frailty index are the most commonly used methods to diagnose frailty. Frailty is related to physical, psychological, cognitive, and social dysfunction, and is sometimes caused by chronic disease. Therefore, primary care providers are ideally situated to incorporate the concept of frailty into their practice, as they are champions in comprehensive care. Although the identification and treatment of frailty is not yet standard practice in primary care, primary care physicians must use the electronic frailty index to identify frailty in all the patients aged ≥65 years in the United Kingdom. In Canada, some insurance companies and governments are using a similar program, which is called the Community Actions and Resources Empowering Seniors model. The clinical practice guidelines of the International Conference of Frailty and Sarcopenia Research, as well as some additional references, will be introduced. Here, we review the current literature on how to diagnose and manage frailty in primary care.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".