Associations between a laboratory frailty index and adverse health outcomes across age and sex
Bibliographic record
Abstract
OBJECTIVE: Early frailty may be captured by a frailty index (FI) based entirely on vital signs and laboratory tests. Our aim was to examine associations between a laboratory-based FI (FI-Lab) and adverse health outcomes, and investigate how this changed with age. METHODS: Up to 8988 individuals aged 20+ years from the 2003-2004 and 2005-2006 National Health and Nutrition Examination Survey cohorts were included. Characteristics of the FI-Lab were compared to those of a self-reported clinical FI. Associations between each FI and health care use, self-reported health, and disability were examined in the full sample and across age groups. RESULTS: Laboratory-based FI scores increased with age but did not demonstrate expected sex differences. Women aged 20-39 years had higher FI scores than men; this pattern reversed after age 60 years. FI-Lab scores were associated with poor self-reported health (odds ratio[95% confidence interval]: 1.46[1.39-1.54]), high health care use (1.35[1.29-1.42]), and high disability (1.41[1.32-1.50]), even among those aged 20-39 years. CONCLUSION: Higher FI-Lab scores were associated with poor health outcomes at all ages. Associations in the youngest group support the notion that deficit accumulation occurs across the lifespan. FI-Lab scores could be utilized as an early screening tool to identify deficit accumulation at the cellular and molecular level before they become clinically visible.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".