FRAILTY IN HEALTHY OLDEST OLD: CHARACTERIZING THE FRAILTY INDEX OF SUPER-SENIORS
Bibliographic record
Abstract
Abstract People at advanced ages often have multiple comorbidities and high frailty. We characterized frailty in “Super-Seniors”, individuals 85 or older who have never been diagnosed with cancer, cardiovascular or lung disease, diabetes or dementia. Super-Seniors were enrolled in the Vancouver Healthy Aging Study that consisted of Phase1 (2004-2007; n=486; age=88.6±3.1 years; female=67.5%) and Phase2 (2014-2019; n=167; age=89.2±3.8 years; female=65.3%). A frailty index (FI) that assesses the accumulation of health deficits was calculated as the proportion of deficits present over those considered (here, 30). The FI distribution patterns, mean, median, 99% limit values, relationship to age, and sex differences were analyzed. The FI of Super-Seniors is right-skewed, with a mean of 0.19±0.09 (median=0.17; limit=0.54) in Phase1 and 0.22±0.08 (median=0.21; limit=0.47) in Phase2. Most Super-Seniors (79% and 61% in Phases 1 and 2) had ≤8 of the 30 deficits; FI≤0.24. The FI increased with age (r’s=0.29 and 0.24); women showed a higher mean FI than men. Data demonstrated the known and consistent characteristics of the FI. The Super-Seniors, who are healthier than the general population of oldest old, have a significantly lower FI that is more typical of individuals aged about 65. The low FI of these healthy oldest old is consistent with their health and high physical and cognitive function, and underscores their suitability for study as a healthy aged group. Further research will investigate how the FI of Super-Seniors is related to lifestyle and genetic factors and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".