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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".