Frailty index of deficit accumulation and falls: data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) Hamilton cohort
Bibliographic record
Abstract
BACKGROUND: To investigate the association between frailty index (FI) of deficit accumulation and risk of falls, fractures, death and overnight hospitalizations in women aged 55 years and older. METHODS: The data were from the Global Longitudinal Study of Osteoporosis in Women (GLOW) Hamilton Cohort. In this 3-year longitudinal, observational cohort study, women (N=3,985) aged ≥ 55 years were enrolled between May 2008 and March 2009 in Hamilton, Canada. A FI including co-morbidities, activities of daily living, symptoms and signs, and healthcare utilization was constructed using 34 health deficits at baseline. Relationship between the FI and falls, fractures, death and overnight hospitalizations was examined. RESULTS: The FI was significantly associated with age, with a mean rate of deficit accumulation across baseline age of 0.004 or 0.021 (on a log scale) per year. During the third year of follow-up, 1,068 (31.89%) women reported at least one fall. Each increment of 0.01 on the FI was associated with a significantly increased risk of falls during the third year of follow-up (odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.02-1.03). The area under the curve (AUC) of the predictive model was 0.69 (95% CI: 0.67-0.71). Results of subgroup and sensitivity analyses indicated the relationship between the FI and risk of falls was robust, while bootstrap analysis judged its internal validation. The FI was significantly related to fractures (hazard ratio [HR]: 1.02, 95% CI: 1.01-1.03), death (OR: 1.05, 95% CI: 1.03-1.06) during the 3-year follow-up period and overnight hospitalizations (incidence rate ratio [IRR]: 1.02, 95% CI: 1.02-1.03) for an increase of 0.01 on the FI during the third year of follow-up. Measured by per standard deviation (SD) increment of the FI, the ORs were 1.21 and 1.40 for falls and death respectively, while the HR was 1.17 for fractures and the IRR was 1.18 for overnight hospitalizations respectively. CONCLUSION: The FI of deficit accumulation increased with chronological age significantly. The FI was associated with and predicted increased risk of falls, fractures, death and overnight hospitalizations significantly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".