Incidence of hip fractures in persons with and without Parkinson’s disease in Finland: a 15-year longitudinal study
Bibliographic record
Abstract
Despite growing evidence on prodromal symptoms of Parkinson's disease, it remains unknown whether they culminated in clinically significant events. We found elevated risks of hip fractures from 3 years before until up to 10 years after diagnosis of Parkinson's disease, underscoring the need for early and continuous fracture risk management. PURPOSE: Parkinson's disease (PD) is associated with a higher risk of hip fracture. However, the risk of hip fracture at the prodromal stage of PD is unknown. Our study aimed to investigate the incidence of hip fractures in persons with and without PD in Finland from 5 years before to 10 years after the diagnosis of PD. METHODS: We included persons diagnosed with PD between 2000 and 2009 in Finland and 1:2 matched comparison cohort. Hazard ratio [HR] was computed to assess overall risk of incident hip fracture. The annual incidence rate per 1000 person-year [IR/1000PY] and incidence rate ratios [IRR] were calculated for each year of follow-up from 5 years before to 10 years after PD diagnosis. RESULTS: During the follow-up among 33,153 eligible persons, 13.4% persons with and 5.3% persons without PD had an incident hip fracture (HR 1.82, 95% confidence interval [CI] 1.47-2.26). Persons with PD had higher annual incidence rates of hip fracture starting from 3 years before diagnosis (with PD: IR/1000PY 2.83, 95% CI 1.93-4.02; without PD: IR/1000PY 1.64, 95% CI 1.15-2.28). The higher annual incidence rates continued until 10 years after diagnosis (with PD: IR/1000PY 15.7, 95% CI 11.7-20.5; without PD: IR/1000PY 4.53, 95% CI 3.08-6.44). Prevalence of fall-risk-increasing medications but not anti-osteoporosis medications was higher at hip fractures among persons with PD than without PD. CONCLUSION: As the risk of hip fracture is elevated already years before the diagnosis of PD, it is important to manage fall risks concomitantly while applying the diagnostic procedures for PD.
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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) | 0.001 | 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 | 0.001 | 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, 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".