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Prevalencia de quedas de idosos em situacao de fragilidade

2013· article· pt· W2059364264 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRevista de Saúde Pública · 2013
Typearticle
Languagept
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.005

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.

Opus teacher head0.022
GPT teacher head0.337
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it