Contribution of Frailty Markers in Explaining Differences Among Individuals in Five Samples of Older Persons
Bibliographic record
Abstract
Frailty is acknowledged to be a state of decreased reserve and decline in multiple physiologic systems, resulting in an increased risk of adverse outcomes (1–7). Nevertheless, there remains debate on its characteristics (1). Understanding the relationship among proposed characteristics is necessary to elucidate whether these characteristics could form a construct of frailty and how such characteristics interrelate. The International Database Inquiry on Frailty (FrData) is an initiative aimed at improving our understanding of seven frailty domains: nutrition, physical activity, mobility, strength, energy, cognition, and mood. Selection of domains was based on a literature review implemented by the Canadian Initiative on Frailty and Aging (8) and based on clinical and biological plausibility. Using the methodology developed in a previous publication (9), the aim of this study is to explore associations among domains using data from five samples of older persons from the United States, Netherlands, Mexico, and Canada. The specific goals of this study are to investigate the aggregation of seven frailty markers as well as the relative importance of each marker in explaining differences among participants. Identifying frailty markers that are consistently more important in explaining differences among older persons, despite how these markers are measured across studies, could lead to considering the relative weight of frailty markers in future research studies and to developing a shorter clinical assessment tool.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".