Measuring frailty using self-report and test-based health measures
Bibliographic record
Abstract
BACKGROUND: previously, frailty indices were constructed using mostly subjective health measures. The reporting error in this type of measure can have implications on the robustness of frailty findings. OBJECTIVE: to examine whether frailty assessment differs when we construct frailty indices using solely self-reported or test-based health measures. DESIGN: secondary analysis of data from The Irish LongituDinal study on Ageing (TILDA). SUBJECTS AND METHODS: 4,961 Irish residents (mean age: 61.9 ± 8.4; 54.2% women) over the age of 50 years who underwent a health assessment were included in this analysis. We constructed three frailty indices using 33 self-reported health measures (SRFI), 33 test-based health measures (TBFI) and all 66 measures combined (CFI). The 2-year follow-up outcomes examined were all-cause mortality, disability, hospitalisation and falls. RESULTS: all three indices had a right-skewed distribution, an upper limit to frailty, a non-linear increase with age, and had a dose-response relationship with adverse outcomes. Levels of frailty were lower when self-reported items were used (SRFI: 0.12 ± 0.09; TBFI: 0.17 ± 0.15; CFI: 0.14 ± 0.13). Men had slightly higher frailty index scores than women when test-based measures were used (men: 0.17 ± 0.09; women: 0.16 ± 0.10). CFI had the strongest prediction for risk of adverse outcomes (ROC: 0.64-0.81), and age was not a significant predictor when it was included in the regression model. CONCLUSIONS: except for sex differences, characteristics of frailty are similar regardless of whether self-reported or test-based measures are used exclusively to construct a frailty index. Where available, self-reported and test-based measures should be combined when trying to identify levels of frailty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".