Frailty and Related Factors in Hospitalized Older People in Northern Cyprus
Bibliographic record
Abstract
Objective:This study aimed to evaluate frailty in hospitalized older people and to identify the related factors. Method:The descriptive study was conducted on 60 older people (66–88 years) who received inpatient treatment in geriatric clinics of two hospitals between September and December 2020. Ethics committee approval, institutional consent, and informed patient consent were obtained for the study. Along with the Edmonton Frailty Scale, a data form was used to collect data about the patient’s socio-demographic, disease characteristics, and frailty risk factors. The data were collected through face-to-face interviews. Results:Frailty of various levels mildly 51.6% and moderately 36.6% of the older people hospitalized in geriatric units in Northern Cyprus was detected (Edmonton Frailty Scale score of 9.23 ± 1.49). The older people in the advanced age (85 years and above) group had an even higher frailty level with the score of 10.0 (p = .009). Those who self-rated as “bad” had either a low education level, were living without a partner, had two chronic diseases, had to use four to seven drugs daily, had a health problem within the last 15 days, had to visit the hospital in the last year, or had to be hospitalized, and had higher min-max Edmonton Frailty Scale scores (p > .05). Conclusion:The frailty levels in older people hospitalized in geriatric units were found to be higher. The older people were classified as frail because of the number of frailty risk factors such as weight loss, weakness, lack of appetite, or had more than three falls.
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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 | 0.001 | 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".