Comparison of two frailty measures in the Conselice Study of Brain Ageing
Bibliographic record
Abstract
OBJECTIVES: Uncertainty about the definition of frailty is reflected by the development of many ways to identify frail people. We aimed to compare the validity of two frailty measures in participants of the Conselice Study of Brain Aging. DESIGN: Prospective population-based study with 4 year follow up. PARTICIPANTS/SETTING: 1,016 subjects aged 65 and over in a rural Italian population. METHODS: For each participant, a Frailty Index (FI) and a Conselice Study of Brain Aging Score (CSBAS) were determined. The FI was created from 43 deficits according to a standardized methodology; 7 variables derived from a previously validated Easy Prognostic Score comprised the CSBAS. RESULTS: The FI had characteristic properties described in other population samples, with a gamma distribution, a 99% limit of about 0.64 and higher values in women than men. CSBAS and FI were strongly correlated with each other (r = 0.72) and both correlated with age (r = 0.32, r = 0.27, respectively). Each was independently predictive of death in a multivariate model, with greater specificity and sensitivity than age alone. CONCLUSIONS: Frailty can be measured by different tools and facilitates a more direct quantification of individual vulnerability than chronological age alone. Though the Frailty Index and the Conselice Study of Brain Aging Score are underpinned by different rationales, clinical utility will continue to motivate their development.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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".