Relation between fractures and mortality: results from the Canadian Multicentre Osteoporosis Study
Bibliographic record
Abstract
BACKGROUND: Fractures have largely been assessed by their impact on quality of life or health care costs. We conducted this study to evaluate the relation between fractures and mortality. METHODS: A total of 7753 randomly selected people (2187 men and 5566 women) aged 50 years and older from across Canada participated in a 5-year observational cohort study. Incident fractures were identified on the basis of validated self-report and were classified by type (vertebral, pelvic, forearm or wrist, rib, hip and "other"). We subdivided fracture groups by the year in which the fracture occurred during follow-up; those occurring in the fourth and fifth years were grouped together. We examined the relation between the time of the incident fracture and death. RESULTS: Compared with participants who had no fracture during follow-up, those who had a vertebral fracture in the second year were at increased risk of death (adjusted hazard ratio [HR] 2.7, 95% confidence interval [CI] 1.1-6.6); also at risk were those who had a hip fracture during the first year (adjusted HR 3.2, 95% CI 1.4-7.4). Among women, the risk of death was increased for those with a vertebral fracture during the first year (adjusted HR 3.7, 95% CI 1.1-12.8) or the second year of follow-up (adjusted HR 3.2, 95% CI 1.2-8.1). The risk of death was also increased among women with hip fracture during the first year of follow-up (adjusted HR 3.0, 95% CI 1.0-8.7). INTERPRETATION: Vertebral and hip fractures are associated with an increased risk of death. Interventions that reduce the incidence of these fractures need to be implemented to improve survival.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".