Comparison of Different Approaches to Operationalize Fried’s Phenotypic Frailty in the NuAge Cohort
Bibliographic record
Abstract
Abstract Many operationalization approaches were proposed to identify frailty in older adults. The common use of Fried’s original criteria or other cut-offs based on cohort distribution may not apply in every cohort leading to potential bias in the identification of frail individuals. We thus aimed to apply different Fried’s phenotypic frailty operationalization approaches in the Quebec NuAge cohort of generally healthy community-dwelling older adults (n=1,753; aged 67-84 years), and longitudinally compare prevalence, incidence and predictive strength on outcomes, such as functional autonomy, falls, hospitalization and mortality. Significant variability in prevalence, classification agreement and predictive strengths were observed between approaches, notably using different types of distribution cut-offs, variables, or ways to handle missing data. This strategy helped us to prioritize a specific Fried’s phenotypic frailty operationalization in NuAge, which could then be used in secondary research projects aiming to study determinants of Fried’s phenotypic frailty and its role in health outcomes.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".