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Record W4403655195 · doi:10.32598/jnrcp.2405.1103

Nutritional knowledge and related factors among hemodialysis patients: A systematic review

2024· review· en· W4403655195 on OpenAlexaff
Seyed Ali Taheri Hatkehlouei, Stephanie Sandanasamy, Nilufer Yildirim, Phil McFarlane

Bibliographic record

VenueJournal of Nursing Reports in Clinical Practice · 2024
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHemodialysisSystematic reviewMedicineIntensive care medicineEnvironmental healthMEDLINEInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

This systematic review examined the nutritional knowledge and related factors among hemodialysis patients. An exhaustive and methodical exploration was conducted across various global electronic databases, including Scopus, PubMed, Web of Science, and Persian electronic databases like Iranmedex and the Scientific Information Database. This search utilized keywords derived from Medical Subject Headings, specifically “knowledge”, “hemodialysis”, and “nutrition”, spanning from the inception of these databases until March 25, 2024. The quality of the studies incorporated into this systematic review was assessed utilizing the Appraisal tool for Cross-Sectional Studies (AXIS tool). In total, there were 721 hemodialysis patients in eight cross-sectional studies. Among the participants, 61.70% were male. The participants had a mean age of 55.03 (standard deviation [SD]=14.06) years. The studies incorporated in this systematic review were conducted in various countries: the United Kingdom (n=2), India (n=2), Italy (n=1), Iran (n=1), Greece (n=1), and a joint study from Turkey and Finland (n=1). The nutritional knowledge among hemodialysis patients was 47.79 out of 100. This score indicates a suboptimal level of knowledge in this area. Factors including education level, attitude, practice, family income per month, and diet counseling were related to the nutritional knowledge of hemodialysis patients. Consequently, policymakers and healthcare administrators can enhance the nutritional understanding of patients undergoing hemodialysis by focusing more on nutritional knowledge factors, such as education level, attitude, practice, monthly family income, and dietary counseling.

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)0.010
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.093
GPT teacher head0.482
Teacher spread0.389 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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