Contribution of Frailty Markers in Explaining Differences Among Individuals in Five Samples of Older Persons
Bibliographic record
Abstract
BACKGROUND: There has been little research on the relative importance of frailty markers. The objective was to investigate the association among seven frailty domains (nutrition, physical activity, mobility, strength, energy, cognition, and mood) and their relative contribution in explaining differences among individuals in five samples of older persons. METHODS: Data from five studies of aging were analyzed using multiple correspondence analysis. Aggregation of frailty markers was evaluated using graphical output. Decomposition of variability was used to assess the relative contribution of each marker in each sample. Results were combined across the samples to assess the average contribution. RESULTS: Frailty markers were found to consistently aggregate in each sample, suggesting a possible underlying construct. Physical strength had the highest contribution on average in explaining differences among individuals. Mobility and energy also had large contributions. Nutrition and cognition had the smallest contributions. CONCLUSIONS: Our results provide further evidence supporting the notion that frailty domains may belong to a common construct. Physical strength may be the most important discriminating characteristic.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".