Fracture risk in long term care: a systematic review and meta-analysis of prospective observational studies
Bibliographic record
Abstract
BACKGROUND: The risk factors associated with fractures have been well-characterized in community dwelling populations, but have not been clearly defined in long-term care (LTC) settings. The objective of this review was to identify risk factors for fractures in LTC settings. METHODS: We searched MEDLINE, the Cochrane Library, EMBASE and CINAHL up to June 2014, scanned reference lists of articles and consulted with experts in the field to identify relevant prospective cohort studies that evaluated risk factors associated with fracture incidence in LTC. We included studies that assessed the association between risk factors included in the WHO-Fracture Risk Assessment Tool (FRAX®) or other predictors relevant to LTC (psychotropic medications, cognitive impairment, mobility, and falls). All articles were screened and extracted by two authors. Available data on the association between a given risk factor and fracture incidence were pooled when possible. We used the GRADE criteria to provide a summary of evidence. The GRADE approach defines the quality of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the quantity of specific interest. RESULTS: We identified 13 prospective cohort studies which examined fracture incidence among LTC residents. Most predictors showed moderate increases in fracture risk, but the quality of the evidence was often low. Moderate quality evidence showed that prior fractures and falls may moderately increase the risk of fractures. Being a woman and cognitive impairment are probably associated with a small increase. The effect of mobility and psychotropic medication use is still uncertain primarily due to the various definitions used in the studies and difficulty summarising the results. CONCLUSIONS: In addition to criteria used in the FRAX assessment tool, such as a previous fracture and female gender, we found that falls and cognitive impairment are also associated with a small to moderate increases in the risk of fractures in LTC. Developing an assessment tool that includes risk factors that are specific to LTC may improve the identification of individuals who can benefit from fracture prevention programs in these settings.
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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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".