Loss of health related quality of life following low-trauma fractures in the elderly
Bibliographic record
Abstract
BACKGROUND: To estimate the long-term change in health related quality of life (HRQoL) following low-trauma fractures among individuals receiving home care (HC) services or living in long-term care (LTC) facilities using linked healthcare administrative data from Ontario, Canada. METHODS: HRQoL was estimated using the Health Utility Index (HUI-2) with the InterRai Minimum Data Set (MDS), a mandatory questionnaire for LTC and HC in the province of Ontario (population 14 million). The HUI-2, a validated HRQoL instrument, allows the calculation of health utility where 0 represents death and 1 the best imaginable health state. For reference, the HUI-2 utility value for Canadians aged 80-84 years is 0.61 and the minimal clinically important difference is 0.03. The MDS was linked to Ontario acute care databases for fiscal years 2007-2011 to identify low-trauma fractures using ICD-10-CA codes. Regression models were used to identify predictors of change in HRQoL from pre-fracture levels to 3 years post fracture for several populations. Low-trauma fractures included hip, humerus, vertebral, wrist, multiple and other. RESULTS: Twenty-three thousand six-hundred fifty-five unique patients with low-trauma fractures were identified with pre- and post-fracture HRQoL assessments, of which 5057 individuals had at least 3 years of follow-up. Compared to patients receiving HC services (N = 3303), individuals residing in LTC (N = 1754) were older, taking more medications, and had more comorbidities. LTC patients had more hip fractures (49 % of total versus 29 %). For all fracture types, HRQoL decreased immediately following fracture. Although levels rebounded after the first month, HRQoL up to 36 months never returned to pre-fracture levels even for non-hip fracture. For both HC and LTC cohorts, clinically important and statistically significant decreases in HUI-2 utility scores were observed 36 months post fracture. Of the 6 HUI-2 domains, mobility had the largest impact on change in HRQoL. Regression analysis indicated that living with a musculoskeletal disorder or a neurological condition and living in LTC were associated with greater decrements in utility following a fracture. CONCLUSIONS: Based on the analysis of one of the largest studies on HRQoL to date, among individuals living in LTC facilities or receiving HC services, fractures have a significant permanent impact on HRQoL up to 3 years following fracture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".