Prognostic Factors of Hip Fracture in Elderly: A Systematic Review
Bibliographic record
Abstract
The hip fracture causes significant disabilities in many elderly people. Many studies around the world have identified various risk factors for the hip fracture. The aim of this study was to systematically investigate the risk factors of hip fractures. This study is a systematic review of risk factors for hip fractures. All published papers in English and Persian languages on patients in Iran and other countries between 2002 - 2022 were examined. The search strategy used keywords matching the mesh, including : predictors, hip fracture, and disability. Articles were selected from international databases (PubMed, Proquest ,Web of Sience, Scopus, Google scholar and Persian(Sid,Magiran), and the Newcastle Ottawa Scale was used to assess the risk of bias. The study has identified several factors that were significantly correlated with the risk of hip fracture, including age, cigarette and alcohol consumption, visual and hearing problems, low BMI levels, history of falling, weakness, and diseases such as stroke, cardiovascular disease, high blood pressure, arthritis, diabetes, dementia, Alzheimer's, Parkinson's, liver and kidney diseases, bone density, osteoporosis, vertebral fracture, and hyperthyroidism. However, the study did not find any significant correlations between the consumption of calcium and vitamin D, history of fractures, cognitive disorders, schizophrenia, and household income, and the risk of hip fracture. The results of this study reveal the determining role of some risk factors in hip fracture in older persons. Therefore, it is recommended that health policy makers provide the possibility of early intervention for some changeable factors.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".