Understanding and Measuring Frailty: Insights From the Canadian NuAge and CLSA Cohorts
Bibliographic record
Abstract
Abstract Frailty is one of the most central concepts in geriatrics; nonetheless, multiple definitions and operationalizations abound, and the underlying biology remains a topic of much discussion. Here, we bring together four talks that join questions of understanding with questions of measurement, in order to explore how answering each is necessary to make progress on the other. We cannot measure frailty if we have not understood and defined it, but we cannot understand if we cannot measure it and study it. Turcot et al. present work on operationalizing frailty in the NuAge cohort. Mayo et al. establish a scale to test the extent to which frailty can be operationalized as a ladder rather than a condition, again using the NuAge cohort. Mendo et al. use mediation analyses to understand how grip strength and other aspects of frailty may play a role in the relationship between diabetes and atherosclerosis. Ghachem et al. test the relationship between physiological dysregulation of different systems and different criteria of the Fried model, in order to assess the evidence for frailty as an emergent physiological state. Together, these talks will push the boundaries of how we think about frailty at levels ranging from biological to clinical to operational.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".