Prevalence of frailty in Brazilian older adults: a systematic review and meta-analysis update
Bibliographic record
Abstract
Abstract This systematic review investigates the prevalence of frailty syndrome in Brazil, considering different factors (context, diagnosis tool, and sex). Studies that included non-institutionalized older adults (60 years old) who were recruited from different contexts were considered. Studies must include a frailty assessment and a sample size of more than 281 participants. Indexed publications, dated from 2001 to October 2024, and retrieved from different databases were considered. COVID-19 related studies were excluded. Studies were independently selected by two reviewers and, in case of disagreements, a third reviewer was consulted. Prevalence data of included studies were meta-analyzed using the PERSyst-MA tool. Thirty-seven studies were included, totaling 82,955 participants. Studies were concentrated in the Southeast region (54%) and used the frailty phenotype (56.7%) as a diagnostic measure. The estimated prevalence of frailty was 21.6%, which was influenced by the assessment method and recruitment context. Frailty prevalence was lower when assessed with frailty phenotype criteria (16.7%) than Edmonton Frail Scale (27.7%). Higher frailty prevalence was observed in health services (32.3%) compared to primary care (23.2%) and community (16.1%). Frailty prevalence did not differ between men and women. These findings reveal significant variability in frailty prevalence among Brazilian older adults, influenced by the assessment tool and recruitment context. The present estimated prevalence underscores the substantial burden of this frailty in the Brazilian population. The results highlight the need for standardized frailty assessment methods and targeted public health strategies to address frailty across different settings.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".