Prevalence and factors associated with frailty among elderly residents in urban area: Casino Deportivo, 2020.
Bibliographic record
Abstract
Frail older persons are prone to falls, disability, dependency, hospitalization and death. The aim is to determine the most up-to-date prevalence of frailty among older adults (OA) and to characterize risk factors related to frailty. A cross-sectional study recruiting participants from the family health records of CMF No 17, "Antonio Maceo" who were > 60 years and utilizing recorded functional assessment, calculating frailty status using the Cuban criteria of frailty and assessing for associations using chi-Square and multiple binary regression on SPSS version 27. Most of the 128 participants were female (64.1%), aged between 60-69 years (40.6%), had white skin color (84.4%), were university graduates (31.3%), retired (48.4%) and had chronic illness (group III, 77.3%). The prevalence of frailty status was 5.1% and was associated with older age, skin color, education level and "health status" group. We observed a low frailty prevalence rate which may reflect improved elderly care. The findings on frailty risk factors may prove vital in prevention, screening and treatment.
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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".