Frailty Change and Major Osteoporotic Fracture in the Elderly: Data from the Global Longitudinal Study of Osteoporosis in Women 3-Year Hamilton Cohort
Bibliographic record
Abstract
Investigating the cumulative rate of deficits and the change of a frailty index (FI) chronologically is helpful in clinical and research settings in the elderly. However, limited evidence for the change of frailty before and after some nonfatal adverse health event such as a major osteoporotic fracture (MOF) is available. Data from the Global Longitudinal Study of Osteoporosis in Women 3-Year Hamilton cohort were used in this study. The changes of FI before and after onset of MOF were compared between the women with and without incident MOF. We also evaluated the relationship between risk of MOF, falls, and death and the change of FI and the absolute FI measures. There were 3985 women included in this study (mean age 69.4 years). The change of FI was significantly larger in the women with MOF than those without MOF at year 1 (0.085 versus 0.067, p = 0.036) and year 2 (0.080 versus 0.052, p = 0.042) post-baseline. The FI change was not significantly related with risk of MOF independently of age. However, the absolute FI measures were significantly associated with increased risk of MOF, falls, and death independently of age. In summary, the increase of the FI is significantly larger in the elderly women experiencing a MOF than their peer controls, indicating their worsening frailty and greater deficit accumulation after a MOF. Measures of the FI change may aid in the understanding of cumulative aging nature in the elderly and serve as an instrument for intervention planning and assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".