Comprehensiveness vs Efficiency: A Cross-Sectional Analysis of the Association Between Allostatic Load and the Frailty Index Using the CLSA
Bibliographic record
Abstract
OBJECTIVES: The goal of this study was to investigate the associations between the frailty index (FI) and allostatic load (AL) to determine if allostatic load contains "key" biomarkers of frailty. If so, AL may offer a more concise health measure using only routine, administrative data. DESIGN: A cross-sectional study design was used to assess the relationships between AL and FI and compare these relationships with an extensively validated health measure, self-rated health. SETTING AND PARTICIPANTS: The data used for this study were obtained from the Canadian Longitudinal Study on Aging (CLSA) baseline comprehensive cohort (n = 26,367). METHODS: Simple linear and logistic regression models were built to measure associations between AL and the FI, which was standardized for improved interpretability. Both indices associations with another validated health measure, self-rated health, were then compared. RESULTS: , 0.357-0.643). Both increased FI and AL were also associated with increased odds of reporting poor or fair health. CONCLUSIONS AND IMPLICATIONS: These results suggest that AL and the FI are related, and that allostatic load may indeed contain "key" biomarkers of frailty. Given this, it is reasonable to suggest that AL could be used in at least an equivalent capacity to the FI currently and may represent a more practical and efficient measure of health and health risk.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".