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Record W4406233592 · doi:10.1186/s12882-024-03921-3

The prevalence of frailty among older adults with maintenance hemodialysis: a systematic

2025· review· en· W4406233592 on OpenAlexaboutno aff
Juanjuan Li, Wei Xiao, Lijuan Wang, Miao Zhang, Yurong Ge

Bibliographic record

VenueBMC Nephrology · 2025
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisChecklistStrengthening the reporting of observational studies in epidemiologySubgroup analysisEpidemiologyObservational studyConfidence intervalMEDLINEGerontologyDemographyInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.195
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.280
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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