#1370 Frailty prevalence and short-term outcomes among younger end-stage renal disease patients on maintenance hemodialysis
Bibliographic record
Abstract
Abstract Background and Aims Frailty is a medical condition of reduced function and health, or a state of heightened risk for catastrophic decline in vitality and function which is evident among end-stage renal disease (ESRD) patients. There has been only limited data regarding its occurrence in the younger age group since most of the published frailty studies involved the elderly as their study population. It is important to identify who among the younger hemodialysis patients are frail, since frailty is related to a higher risk of hospitalization and mortality. This study aims to estimate the prevalence of frailty among younger ESRD patients on maintenance hemodialysis using the Edmonton Frail Scale (EFS) and its association with short-term outcomes: hospital consultations, use of emergency room services, hospitalizations, and mortality. Method A prospective single-center cohort study was conducted. All ESRD patients aged 18-64 years old on maintenance hemodialysis on an outpatient basis were included. Data collection included the patients’ demographic data, underlying diseases, comorbid conditions, body mass index, and recent laboratory results. The EFS was administered during the first month of the study, and patients were then monitored for consultations, use of emergency room services, hospitalizations, and mortality in the succeeding months until the end of the study period. Results Out of 166 ESRD patients, 94 belong to the younger age group and 87 patients were included: 33 (37.9%) were frail, 21 (24.1%) were vulnerable, and 33 (37.9%) were fit. Chronic Glomerulonephritis was the leading cause of ESRD in the younger population, and Diabetic Nephropathy was the primary cause among those categorized as frail. Hypertension was the predominant comorbid condition across all categories. Frail patients had the greatest number of hospital consultations (54%) and had frequent utilization of emergency room services. They also exhibited the highest number of hospitalizations consisting 69% of the total admissions across all categories. All deaths in this study came from the patients categorized as frail. Conclusion Frailty is prevalent among younger ESRD patients on maintenance hemodialysis and is associated with increased risk for hospital consultation, use of emergency room services, hospitalization, and mortality.
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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) | 0.003 | 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".