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Record W3033578500 · doi:10.1093/ndt/gfaa142.p1584

P1584FRAILTY IN MAINTENANCE HEMODIALYSIS PATIENTS

2020· article· en· W3033578500 on OpenAlexaboutno aff
Zauresh Amreyeva, Gulnar Chingayeva, Abay Shepetov, Assiya Kanatbayeva, Arina Yespotayeva

Bibliographic record

VenueNephrology Dialysis Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnthropometryHemodialysisDialysisMalnutritionBody mass indexWaistDiabetes mellitusPopulationPhysical therapyInternal medicineGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background and Aims The population in Kazakhstan is rapidly aging, as a result the number of geriatric patients on maintenance hemodialysis (MHD) has been increasing. Frailty is prevalent in dialysis patients and is one of the common factors that can lead to increased morbidity and mortality. The primary objectives of this study were to evaluate the prevalence of frailty in elderly patients on MHD by using Edmonton Frailty Scale and assess their association with clinical and laboratory measurements. A secondary objective was to investigate the relationship between nutritional status and frailty. Method From July to September 2018, a total of 65 elderly patients undergoing HD in 7 dialysis facilities in Almaty, Kazakhstan were enrolled in this cross-sectional study. All participants were evaluated for the cognitive status through Mini-Mental State Examination (MMSE), nutritional status by using Mini Nutritional Assessment (MNA), Malnutrition-Inflammation Score (MIS), and anthropometric measurements (body mass index (BMI), triceps skinfold (TSF), mid-arm muscle circumference (MAMC)), functionality (Handgrip strength), as well biochemical data were collected from medical records. Frailty was defined in accordance with the Edmonton Frail scale (EFS). Results The study participants’ median age was 69 (range: 65–88) years old, and median dialysis vintage was 36 (IQR 15–60) months, 53.8% were female. The main comorbidities were hypertension (69.2%) and diabetes (35.4%). The prevalence of frailty assessed by the EFS was 23.1% (men: 13.3%; women: 86.7), 43.1% patients were non-frail (men: 64.3%; women: 35.7%), 33.8% patients were vulnerable (men: 45.5%; women: 54.5%). Based on MIS the prevalence of PEW was 73.8% and, according to MNA, the risk of malnutrition was detected in 47.7%, and 9.2% had malnutrition. No significant difference was observed between genders in the frequency of PEW. Mean body weight was 69.1±11.3kg, the mean BMI was slightly overweight 25.6±4.29kg/m2, while hand-grip strength was 21.33±3.36 in men and 15.5±5.51 in women, p=0.008, and it is lower than the normal population standard values. The frail patients group had a higher proportion of women 86.7% (p=0.001), worse nutritional status (93.3% and 86,7% had PEW evaluated by MIS (p=0.018) and MNA (p=0.035), respectively), more frequency of falls (p=0.01), anemia (p=0.038) when compared to group of non-frail and vulnerable patients. 66.7% of frail patients were widowed (p=0.005). The mean MMSE in this group of patients was 26.7±1.9. Conclusion The prevalence of frailty among elderly hemodialysis patients in this study was 23.1%, and we detected that 86.7% of them were female, as well PEW increased in frail patients. Also the study showed that protein-energy wasting is common among elderly hemodialysis patients. Its prevalence varies between 73.8% and 56.9% depending on the measurement tool used to evaluate the nutritional status. In our country with limited resources, EFS, MIS and MNA could help to follow elderly hemodialysis patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.245
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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