Frailty Clinical Phenotype: A Physical and Cognitive Point of View
Bibliographic record
Abstract
Frailty is recognized as a clinical geriatric syndrome used to describe the weakest or most vulnerable older adults. Although the term frailty is commonly used in clinical practice, and the theoretical phenomenon is well accepted, it remains an evolving concept that lacks a universally accepted definition and specific diagnostic criteria. Different perspectives on frailty have led to two distinct viewpoints of this phenomenon in the literature. The first describes the phenomenon based solely on physical attributes and capabilities. In contrast, more recent perspectives describe the phenomenon in broader, multidimensional terms by incorporating the concept of cognitive frailty. In support of this view, there is increasing evidence that consideration of both cognitive and physical factors can better improve the ability to predict adverse health outcomes among frail older adults over physical factors alone. The recent recognition of the importance of cognitive factors has increased the complexity of this phenomenon and difficulty in developing a consensus definition. To add to this challenge, frailty can present in different stages of severity (from mild to severe), and there appears to be a dynamic relationship between these stages. Despite these challenges, a consensus on an international definition of frailty including physical and cognitive criteria is essential in order to advance research and treatment of this condition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".