Implementing Frailty Into Clinical Practice: A Cautionary Tale
Bibliographic record
Abstract
BACKGROUND: Little is known about the contribution of frailty in improving patient-level prediction beyond readily available clinical information. The objective of this study is to compare the predictive ability of 129 combinations of seven frailty markers (cognition, energy, mobility, mood, nutrition, physical activity, and strength) and quantify their contribution to predictive accuracy beyond age, sex, and number of chronic diseases. METHODS: Two cohorts from the Established Populations for Epidemiologic Studies of the Elderly were used. The model with the best predictive fit in predicting 6-year incidence of disability was determined using the Akaike Information Criterion. Predictive accuracy was measured by the C statistic. RESULTS: Incident disability was 23% in one cohort and 20% in the other cohort. The "best model" in each cohort was found to be a model including between five and seven frailty markers including cognition, mobility, nutrition, physical activity, and strength. Predictive accuracy of the 129 models ranged from 0.73 to 0.77 across both cohorts. Adding frailty markers to age, sex, and chronic disease increased predictive accuracy by up to 3% in both cohorts (p < .001). The contribution of frailty increased up to 9% in the oldest age group. CONCLUSIONS: Adding frailty markers provided a modest increase in patient-level prediction of disability. Such a modest increase may still be worthwhile because while age, sex, and the number of chronic diseases are not modifiable, frailty may be. Further studies examining the contribution of frailty in improving prediction are needed before adopting frailty as a prognostic 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 | 0.266 | 0.441 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.020 | 0.062 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".