Individuals with neurological diseases are at increased risk of fractures within 180 days of admission to long-term care in Ontario
Bibliographic record
Abstract
BACKGROUND: Individuals residing in long-term care (LTC) are more likely to have a fragility fracture than community-dwelling seniors. The purpose of this study was to determine whether the presence of neurological diseases was associated with an increased risk of fracture within 180 days of admission to LTC. METHODS: This retrospective cohort study used data collected in the LTC setting using the Resident Assessment Instrument (RAI) 2.0 during the period from 2006 to 2011 (N=42,089). Multivariable logistic regression analyses were conducted to determine the associations between the presence of neurological conditions and incident fractures, with and without adjustment for clinical variables. RESULTS: The incident fracture rate for all LTC residents was 2.6% (N=1,094). Neurological condition group size ranged from n=21,015 for Alzheimer's disease or related dementias (ADRD) to n=21 for muscular dystrophy (MD). The incidence of fracture among residents with specific neurological diseases was as follows: ADRD, 3.2% (n=672), MD, 4.8% (n=1), Parkinson's disease, 2.5% (n=57), stroke, 2.3% (n=166), epilepsy, 2.5% (n=38), Huntington's disease, 1.4% (n=1), multiple sclerosis, 0.3% (n=1) and traumatic brain injury, 3.8% (n=11); among the comparison group with no neurological conditions, the fracture rate was 2.0% (n=366). The neurological diseases that were associated with a significantly greater odds of having an incident fracture in the first 180 days of LTC admission were as follows: ADRD (1.3; 95% CI: 1.1-1.5), epilepsy (1.5; 95% CI: 1.0-2.1) and traumatic brain injury (2.7; 95% CI: 1.4-5.0). CONCLUSION: LTC residents with ADRD, epilepsy and traumatic brain injury are at a higher risk for sustaining an incident fracture in the first 180 days of admission and should be considered for fracture prevention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".