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Record W4214813993 · doi:10.1177/20543581221077505

Update on Existing Care Models for Chronic Kidney Disease in Low- and Middle-Income Countries: A Systematic Review

2022· review· en· W4214813993 on OpenAlexaff
Victoria Nkunu, Natasha Wiebe, Aminu K. Bello, Sandy Campbell, Elliot Koranteng Tannor, Cherian Varghese, John W. Stanifer, Marcello Tonelli

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2022
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersWorld Health Organization
KeywordsMedicineReferralKidney diseaseMultidisciplinary approachMEDLINEHealth careFamily medicinePopulationPediatricsEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Approximately 78% of chronic kidney disease (CKD) cases reside in low- and middle-income countries (LMICs). However, little is known about the care models for CKD in LMICs. OBJECTIVE: Our objective was to update a prior systematic review on CKD care models in LMICs and summarize information on multidisciplinary care and management of CKD complications. DESIGN: We searched MEDLINE, EMBASE, and Global Health databases in September 2020, for papers published between January 1, 2017, and September 14, 2020. We used a combination of search terms, which were different iterations of CKD, care models, and LMICs. The World Bank definition (2019) was used to identify LMICs. SETTING: Our review included studies published in LMICs across 4 continents: Africa, Asia, North America (Mexico), and Europe (Ukraine). The study settings included tertiary hospitals (n = 6), multidisciplinary clinics (n = 1), primary health centers (n = 2), referral centers (n = 2), district hospitals (n = 1), teaching hospitals (n = 1), regional hospital (n = 1), and an urban medical center (n = 1). PATIENTS: . MEASUREMENTS: We retrieved the following details about CKD care: funding, urban or rural location, types of health care staff, and type of care provided, as defined by Kidney Disease Improving Global Outcomes (KDIGO) guidelines for CKD care. METHODS: We included studies which met the following criteria: (1) population was largely adults, defined as age 18 years and older; (2) most of the study population had CKD, and not end-stage kidney disease (ESKD); (3) population resided in an LMIC as defined by the World Bank; (4) manuscript described in some detail a clinical care model for CKD; (5) manuscript was in either English or French. Animal studies, case reports, comments, and editorials were excluded. RESULTS: Eighteen studies (24 care models with 4665 patients) met inclusion criteria. Out of 24 care models, 20 involved interdisciplinary health care teams. Twenty models incorporated international guidelines for CKD management. However, conservative kidney management (management of kidney failure without dialysis or renal transplant) was in a minority of models (11 of 24). Although there were similarities between all the clinical care models, there was variation in services provided and in funding arrangement; the latter ranged from comprehensive government funding (eg, Sri Lanka, Thailand), to out-of-pocket payments (eg, Benin, Togo). LIMITATIONS: These include (1) lack of detail on CKD care in many of the studies, (2) small number of included studies, (3) using a different definition of care model from the original Stanifer et al paper, and (4) using the KDIGO Guidelines as the standard for defining a CKD care model. CONCLUSIONS: Most of the CKD models of care include the key elements of CKD care. However, access to such care depends on the funding mechanism available. In addition, few models included conservative kidney management, which should be a priority for future investment. TRIAL REGISTRATION: Not applicable.

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)0.018
metaresearch head score (Gemma)0.072
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 score0.023
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.072
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0090.012
Bibliometrics0.0230.017
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0040.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.048
GPT teacher head0.334
Teacher spread0.287 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Kidney Health and DiseaseSame topicChronic Kidney Disease and DiabetesFrench-language works237,207