Femur fracture: causes and profile of hospitalized elderly in Pelotas/RS, Brazil
Bibliographic record
Abstract
Femur fracture is among the most common traumatic injuries in the elderly population, it presents a high mortality rate in the first year after fracture, it causes loss of functional capacity, resulting in about half of the elderly being unable to walk and in a quarter the need of extended care at home. The main objective of this research was to describe the causes of femur fractures and the profile of older adults with a diagnosis of femur fracture, hospitalized by SUS in the city of Pelotas / RS, Brazil. We opted for a cross-sectional descriptive study with elderly people who had femur fractures and were hospitalized in the Santa Casa de Pelotas Hospital, from February to August 2012. The variables were: Socioeconomic, demographic, and related to health / illness, besides the Mini Mental State Examination (MMSE). We interviewed 50 individuals, 39 women and 11 men. From the elderly fractured, mostly were white, retired, widowed, could read and write, and living with someone. Morbidity was prevalent hypertension with 56% of the sample, the majority reported continuous use of drugs and physical inactivity. The predominant mechanism of injury was falls occurred in the home of the elderly, and 78% of the elderly reported difficulty in walking before fracture. Regarding the tracking signs of dementia, 84% of elderly patients with femur fractures had cognitive impairment. Through the results presented in this research, and the aging of the population living in Brazil and in the city of study, it is relevant falls prevention campaigns among the elderly, with consequent promotion of improvement in the quality of life in this population.
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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".