Evaluation of Risk Factors for Second Hip Fractures in Elderly Patients
Bibliographic record
Abstract
BACKGROUND: Hip fracture is a worldwide public health problem that primarily affects osteoporotic individuals and the elderly. A second hip fracture can occur in elderly patients who have already suffered an initial hip fracture. The aim of this study was to investigate possible risk factors for second hip fractures in elderly patients with hip fractures. METHODS: Between 2010 and 2014, 230 patients who underwent uncemented bipolar hemiarthroplasty for hip fractures were retrospectively analyzed. The patients were divided into two groups: those with a first hip fracture (group 1) and those with a second hip fracture (group 2). RESULTS: The mean time from the first hip fracture to second hip fracture was 22 months. There were no significant differences in the American Society of Anesthesiologist scores, comorbidities were observed in the two groups. The mean length of hospitalization was not significantly different between the two groups. The mean postoperative functional scores after second hip fractures were significantly lower in group 2 than in group 1. CONCLUSIONS: Although there are not certain risk factors for second hip fractures in elderly patients with hip fractures, to prevent second hip fractures, elderly patients should be provided with physical and medical therapy as well as orthotic support and their functional activity should be maintained.
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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 | 0.001 |
| 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) | 0.001 | 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".