Association of Frailty Comorbidity with Incidence of Fractures among Elderly at Assiut Trauma University Hospital
Bibliographic record
Abstract
Background: Frailty is a growing public health concern, impacts clinical care significantly. As the elderly population expands, frailty rates are expected to increase. Bone fractures are a public health issue especially in elderly people that lead to disability, impaired quality of life, and high health-care costs. Aim: To assess the association of frailty comorbidity with incidence of fractures among elderly. Research design: A descriptive cross-sectional research design. Setting: inpatient ward and outpatients' clinics in Trauma Hospital at Assiut University. Sample: A convenience sample of 321 older adult patients who had Fractures. Study tools: Three tools were selected I: Structured interviewing questionnaire II: Reported Edmonton Frail Scale to assess frailty for elderly patients with fractures and II: Charlson Comorbidity Index scale to assess level of comorbidity. Results: The proportions of falls as a cause of fracture were 81.7% for all fractures and 76.0% of the studied elderly patients fall in their home. The most common fractures in the total population was the hip fracture (48.6%), and femur fracture (22.4%). Most of studied elderly patients have severe frailty (52.7%), moderate frailty (17.4%). This study found statistical significant difference between patient’s comorbidities and frailty at p-value <0.000. Conclusion: The study highlights the association of frailty and incidence of fractures among elderly patients and level of comorbidities. Recommendation: Implement evidence-based rehabilitation programs to improve mobility and reduce complications after fractures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".