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Record W4405236777 · doi:10.1371/journal.pone.0309973

Assessing the quality of CKD care using process quality indicators: A scoping review

2024· review· en· W4405236777 on OpenAlexafffund
N. Zhou, Chengchuan Chen, Yubei Liu, Zhaolan Yu, Aminu K. Bello, Yanhua Chen, Ping Liu

Bibliographic record

VenuePLoS ONE · 2024
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health ResearchKidney Foundation of Canada
KeywordsQuality (philosophy)Process (computing)MedicineComputer scienceEnvironmental healthRisk analysis (engineering)Physics

Abstract

fetched live from OpenAlex

INTRODUCTION: Assessing the quality of chronic kidney disease (CKD) management is crucial for optimal care and identifying care gaps. It is largely unknown which quality indicators have been widely used and the potential variations in the quality of CKD care. We sought to summarize process quality indicators for CKD and assess the quality of CKD care. METHODS: We searched databases including Medline (Ovid), PubMed, Cochrane Library, Web of Science, CINAHL, and Scopus from inception to June 20, 2024. Two reviewers screened the identified records, extracted relevant data, and classified categories and themes of quality indicators. RESULTS: We included 24 studies, extracted 30 quality indicators, and classified them into three categories with nine themes. The three categories included laboratory measures and monitoring of CKD progression and/or complications (monitoring of kidney markers, CKD mineral and bone disorder, anemia and malnutrition, electrolytes, and volume), use of guideline-recommended therapeutic agents (use of medications), and attainment of therapeutic targets (blood pressure, glycemia, and lipids). Among the frequently reported quality indicators (in five or more studies), the following have a median proportion of study participants achieving that quality indicator exceeding 50%: monitoring of kidney markers (Scr/eGFR), use of medications (ACEIs/ARBs, avoiding non-steroidal anti-inflammatory drugs (NSAIDs)), management of blood pressure (with a target of ≤140/90, or without specific targets), and monitoring for glycated hemoglobin A1c (HbA1c)). The presence of diabetes, hypertension, cardiovascular disease, or proteinuria was associated with higher achievement in indicators of monitoring of kidney markers, use of recommended medications, and management of blood pressure and glycemia. CONCLUSION: The quality of CKD management varies with quality indicators. A more consistent and complete reporting of key quality indicators is needed for future studies assessing CKD care quality.

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)0.066
metaresearch head score (Gemma)0.266
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.066
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.266
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.010
Bibliometrics0.0370.041
Science and technology studies0.0020.002
Scholarly communication0.0080.008
Open science0.0040.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.373
GPT teacher head0.538
Teacher spread0.165 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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