A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course
Why this work is in the frame
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Bibliographic record
Abstract
A frailty index (FI) based entirely on common clinical and laboratory tests might offer scientific advantages in understanding ageing and pragmatic advantages in screening. Our main objective was to compare an FI based on common laboratory tests with an FI based on self-reported data; we additionally investigated if the combination of subclinical deficits with clinical ones increased the ability of the FI to predict mortality. In this secondary analysis of the 2003-2004 and 2005-2006 National Health and Nutrition Examination Survey data, 8888 individuals aged 20+ were evaluated. Three FIs were constructed: a 36-item FI using self-reported questionnaire data (FI-Self-report); a 32-item FI using data from laboratory test values plus pulse and blood pressure measures (FI-Lab); and a 68-item FI that combined all items from each index (FI-Combined). The mean FI-Lab score was 0.15 ± 0.09, the FI-Self-report was 0.11 ± 0.11 and FI-Combined was 0.13 ± 0.08. Each index showed some typical FI characteristics (skewed distribution with long right tail, non-linear increase with age). Even so, there were fewer people with low frailty levels and a slower increase with age for the FI-Lab compared to the FI-Self-report. Higher frailty level was associated with higher risk of death, although it was strongest at older ages. Both FI-Lab and FI-Self-report remained significant in a combined model predicting death. The FI-Lab was feasible and valid, demonstrating that even subclinical deficit accumulation increased mortality risk. This suggests that deficit accumulation, from the subcellular to the clinically visible is a useful construct that may advance our understanding of the ageing process.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it