Hospitalize Yaşlı Hastalarda Kırılganlığın ve Etkilerinin Değerlendirilmesi
Bibliographic record
Abstract
ABSTRACT \n \nNurefşan Cihangiroglu Unal, MD. Evaluation of frailty and its effects in hospitalized geriatric population. Hacettepe University Medical Faculty Department of Internal Medicine, Residency Thesis, Ankara, 2017. Frailty syndrome is seen in high prevalences in hospitalized elderly patients and is usually under diagnosed. In this research our aim is to diagnose frailty in elderly patients that are hospitalized due to acute health problems using readily usable and practical methods and evaluate the effects on bad health outcomes. Fifty seven patients are included in this research that are hospitalized in Hacettepe Internal Medicine wards between March 2017 and September 2017 due to acute health problems that is over 65 years of age. Patients’ frailty measurements are done using Edmonton Frail Scale (EFS) and Clinical Frailty Scale (CFS). Comprehensive geriatric assesment tests were performed. Bioelectric- impedance analysis (BIA) method was used to asses muscle mass and muscle strength was assessed with hand grip measurements. Patients are followed for bad health outcomes. Comprehensive geriatric assesment results were found to be related with frailty in both scales. Spearman correlation analysis showed positive correlation between EFS and comprehensive geriatric assesment tests. Hand grip strength was lower in frail patients and showed significant correlation between EFS scores. Frail patients’ hospitalization periods was found to be longer but this was not statistically significant. There was one mortality in the patients and this patient was in the frail patient population in both scales. \n \nKey Words: Edmonton Frail Scale, hand grip, frailty, length of stay.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".