Portals to frailty? Data-driven analyses detect early frailty profiles
Bibliographic record
Abstract
BACKGROUND: Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. Two prominent approaches for measuring frailty are the frailty phenotype and the frailty index. We explored a complementary data-driven approach for frailty assessment that could detect early frailty profiles (or subtypes) in relatively healthy older adults. Specifically, we tested whether (1) modalities of early frailty profiles could be empirically determined, (2) the extracted profiles were differentially related to longitudinal cognitive decline, and (3) the profile and prediction patterns were robust for males and females. METHODS: Participants (n = 649; M age = 70.61, range 53-95) were community-dwelling older adults from the Victoria Longitudinal Study who contributed data for baseline multi-morbidity assessment and longitudinal cognitive trajectory analyses. An exploratory factor analysis on 50 multi-morbidity items produced 7 separable health domains. The proportion of deficits in each domain was calculated and used as continuous indicators in a data-driven latent profile analysis (LPA). We subsequently examined how frailty profiles related to the level and rate of change in a latent neurocognitive speed variable. RESULTS: LPA results distinguished three profiles: not-clinically-frail (NCF; characterized by limited impairment across indicators; 84%), mobility-type frailty (MTF; characterized by impaired mobility function; 9%), and respiratory-type frailty (RTF; characterized by impaired respiratory function; 7%). These profiles showed differential neurocognitive slowing, such that MTF was associated with the steepest decline, followed by RTF, and then NCF. The baseline frailty index scores were the highest for MTF and RTF and increased over time. All observations were robust across sex. CONCLUSIONS: A data-driven approach to early frailty assessment detected differentiable profiles that may be characterized as morbidity-intensive portals into broader and chronic frailty. Early inventions targeting mobility or respiratory deficits may have positive downstream effects on frailty progression and cognitive decline.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".