Fragilidad en adultos mayores con falla cardiaca crónica en un hospital de Lima
Bibliographic record
Abstract
Objective: To determine the prevalence of frailty in older adults with heart failure and to examine the association between frailty and socio-demographic and clinical characteristics in patients in the Chronic Heart Failure program of the Guillermo Almenara Irigoyen National Hospital in the period 2018-2020. Materials and methods: Observational, cross-sectional, correlational study with quantitative approach in patients older than 60 years who had a frailty assessment using the Edmonton scale; as non-frail (0-4), apparently vulnerable (5-6), mildly frail (7-8), moderately frail (9-10) and severely frail (11-17). The association of frailty and patient characteristics was assessed using Pearson's Chi-Square test, values of p<0.05 and with a 95% confidence interval were considered significant. Results: The prevalence of frailty was 58.8%, most patients were male (71.8%) and the average age was 72.9 years. Age and number of comorbidities were statistically significant factors associated with frailty with p=0.004 and p<0.001 respectively. Conclusions: The prevalence of frailty was high in patients older than 60 years in the chronic heart failure program. Older patients with more comorbidities were at higher risk of frailty, highlighting the need for comprehensive assessment and screening for frailty in order to design secondary prevention programs in a timely manner.
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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 | 0.001 |
| 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".