Frailty in hemodialysis and prediction of poor short-term outcome: mortality, hospitalization and visits to hospital emergency services
Bibliographic record
Abstract
Background: Frailty is an aging-associated state of increased vulnerability, which raises the risk of adverse outcomes. Chronic kidney disease is associated with higher prevalence of frailty. Our aim was to estimate frailty prevalence in a hemodialysis population and its influence on short-term outcomes.Design: Observational prospective longitudinal study of 277 prevalent hemodialysis patients. Frailty was estimated through the Edmonton Frail Scale (EFS). Demographic and clinical data, comorbidity index, and laboratory parameters were recorded. A 29-month follow-up was conducted on mortality, including hospitalization, and visits to hospital emergency services in the first 12 months of this period.Results: According to the EFS, 82 patients (29.6%) were frail, 53 (19.1%) were vulnerable, and 142 (51.3%) were non-frail. During follow-up, 58.5% frail patients, 30.2% vulnerable, and 16.2% non-frail ones died (p < .005). In the analysis of survival using an adjusted Cox model, a higher hazard of mortality was observed in frail than in non-frail patients (HR 2.34; 95% CI 1.39–3.95; p = .001). During follow-up the hospitalization rate was 852 episodes/1000 patient-years for frail patients, 784 episodes/1000 patient-years for vulnerable patients, and 417 episodes/1000 patient-years for non-frail patients (p = .0005). The incidence ratio of visits to emergency services was 3216, 1735, and 1545 visits/1000 patient-years for each group (p < .001).Conclusions: Hemodialysis patients present high frailty prevalence. Frailty is associated with poor short-term outcomes and higher rates of mortality, visits to hospital emergency services, and hospitalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".