Hip fracture incidence and post-fracture mortality in Victoria, Australia: a state-wide cohort study
Bibliographic record
Abstract
Hip fractures are a major public health concern. Number of hip fractures cases increased by 20% from 2012 to 2018. Factors associated with post-fracture mortality included men, those who are frail, living in a non-metropolitan region, or residing in a residential aged care facility. Our results are useful for planning healthcare interventions. PURPOSE: Hip fractures are a major public health concern in Australia. Data on hip fracture incidence and mortality are needed to plan and evaluate healthcare interventions. The aims of the study were to investigate (1) the time-trend in absolute number and incidence of first hip fractures, and (2) factors associated with mortality following first hip fractures in Victoria, Australia. METHODS: A state-wide cohort study of all patients aged [Formula: see text] 50 years admitted to a Victorian hospital for first hip fracture between July 2012 and June 2018. Annual age-standardized incidence rates were calculated using population data from Australian Bureau of Statistics. Multivariate negative binomial regression was used to investigate factors associated with post-fracture mortality. RESULTS: Overall, 31,578 patients had a first hip fracture, of whom two-thirds were women and 47% were [Formula: see text] 85 years old. Absolute annual numbers of first hip fractures increased by 20%. There was no significant change in age- and sex-adjusted incidence. In total, 8% died within 30 days and 25% within 1 year. Factors associated with 30-day mortality included age (≥ 85 years old versus 50-64 years old, mortality rate ratio [MRR] 8.05, 95% confidence interval [CI] 5.86-11.33), men (MRR 2.11, 95% CI 1.88-2.37), higher Hospital Frailty Risk Scores (high frailty versus no frailty, MRR 3.46, 95% CI 2.66-4.50), admission from a residential aged care facility (RACF) (MRR 2.28, 95% CI 1.85-2.82), and residing in a non-metropolitan region (MRR 1.22, 95% CI 1.09-1.38). The same factors were associated with 1-year mortality. CONCLUSION: The absolute increase in hip fractures highlights the need for interventions to reduce fracture risk, especially for those at higher risk of post-fracture mortality, including men and those who are frail, living in a non-metropolitan region, or residing in a RACF.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, 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".