Frailty indices based on self-report, blood-based biomarkers and examination-based data in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Frailty can be operationalised using the deficit accumulation approach, which considers health deficits across multiple domains. We aimed to develop, validate and compare three different frailty indices (FI) constructed from self-reported health measures (FI-Self Report), blood-based biomarkers (FI-Blood) and examination-based assessments (FI-Examination). METHODS: Up to 30,027 participants aged 45-85 years from the baseline (2011-2015) comprehensive cohort of the Canadian Longitudinal Study on Aging were included in the analyses. Following standard criteria, three FIs were created: a 48-item FI-Self Report, a 23-item FI-Blood and a 47-item FI-Examination. In addition a 118-item FI-Combined was constructed. Mortality status was ascertained in July 2019. RESULTS: FI-Blood and FI-Examination demonstrated broader distributions than FI-Self Report. FI-Self Report and FI-Blood scores were higher in females, whereas FI-Examination scores were higher in males. All FI scores increased nonlinearly with age and were highest at lower education levels. In sex and age-adjusted models, a 0.01 increase in FI score was associated with a 1.08 [95% confidence interval (CI): 1.07,1.10], 1.05 (1.04,1.06), 1.07 (1.05,1.08) and a 1.13 (1.11,1.16) increased odds of mortality for FI-Self Report, FI-Blood, FI-Examination and FI-Combined, respectively. Inclusion of the three distinct FI types in a single model yielded the best prognostic accuracy and model fit, even compared to the FI-Combined, with all FIs remaining independently associated with mortality. CONCLUSION: Characteristics of all FIs were largely consistent with previously established FIs. To adequately capture frailty levels and to improve our understanding of the heterogeneity of ageing, FIs should consider multiple types of deficits including self-reported, blood and examination-based measures.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".