The prevalence of frailty among older adults with maintenance hemodialysis: a systematic
Bibliographic record
Abstract
Abstract Background To evaluate the epidemiological data on the prevalence of frailty and prefrailty in individuals aged 60 years or older on MHD patients. Methods PubMed, Web of Science, Embase, CNKI, WanFang, CBM, and VIP were searched from inception to February 2023 using combinations of subject words and free words. The methodological quality of all the selected studies was assessed using the Joanna Briggs Institute Critical Appraisal of Epidemiological Studies Checklist and Newcastle‒Ottawa Cohort Quality Assessment Scale. Random effects meta-analysis was used to pool estimates from different studies. Subgroup analysis and meta-regression were performed to explore potential sources of heterogeneity. Results Of the 4,190 documents retrieved, 16 observational studies involving 2,446 participants from 8 countries were included in this systematic review. Among older adults receiving MHD, the overall prevalence of frailty and prefrailty was 41% (95% CI = 34–49%) and 37% (95% CI = 26–48%), respectively, with considerable heterogeneity. The pooled prevalence of frailty was greater among individuals aged > 70 years (45%) than among those aged ≤ 70 years (37%). However, subgroup analyses indicated that the confidence intervals for the age group overlap substantially. Conclusion Our research showed that the prevalence of frailty and prefrailty in older patients with MHD are high. Trial registration The PROSPERO registration number for this study was CRD42023442569.
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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".