Prevalencia de quedas de idosos em situacao de fragilidade
Bibliographic record
Abstract
OBJECTIVE: To measure the prevalence in frail elderly people, their consequences and associated demographic factors. METHODS: This was an epidemiological and cross-sectional study with a probabilistic sample composed of 240 elderly people (≥ 60 years) living in Ribeirão Preto, Sao Paulo state. Data were collected between November 2010 and February 2011, through a questionnaire that included socio-demographic data, fall assessment and the Edmonton Frailty Scale. Uni-variate and bivariate analyses were carried out. RESULTS: The mean age was 73.5 (± 8.4), with higher ages among women; 25% of the interviewees were aged 80 or older; 11.3% presented moderate frailty and 9.6% severe frailty. The prevalence of falls in frail elderly participants corresponded to 38.6%; higher levels were found among women and younger subjects (60 to 79 years old); 26.8% were victims of 1 to 2 falls, 27.1% of which occurred in the bedroom, 84.7% fell from their own height, 55.9% lost their balance, 54.2% suffered scratches and 78% were afraid of suffering a new fall. Higher fall prevalence levels were found in frail elderly 1,973 (1,094-3,556) compared to non-frail. CONCLUSIONS: We highlight the importance of addressing the health of frail elderly people, especially regarding the risk of falls, as well as of increasing investment in prevention strategies of these syndromes and in the formation of train like a virgin ed human resources to better care for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".