Comprendre le parcours de soins et la gestion de la maladie rénale chronique stades précoce à modéré : une revue systématique de la littérature des années 2000 à 2022 dans les pays à revenus élevés, données de l’Océanie et du Canada
Bibliographic record
Abstract
Context: Chronic Kidney Disease (CKD) is common, with its incidence and prevalence continually increasing. Its management has become a public health concern to delay progression to end-stage and alleviate the burden it places on patients and the healthcare system. Our objective was to describe and synthesize the care pathway and management of CKD at early to moderate stages to optimize patient care. We used a systematic literature review methodology through a project named ChroKiDEL for "Chronic Kidney Disease Early stage Literature review."Materials and methods: we searched databases including PubMed, Embase, Cochrane, PdQ Evidence, and KT+ from the years 2000 to 2022 to identify studies concerning the care pathway and/or management of ambulatory patients over 18 years old with early to moderate CKD. The studies involved primary, secondary, and/or tertiary care in high-income countries. Two evaluators independently performed article selection meeting our inclusion criteria, data extraction, and evaluation of study quality. Identified studies were classified into subgroups by country.Results: the results of this review focused only on data from Canada and Oceania. 16 studies were included. Gaps were identified in early-stage CKD screening and recognition, care coordination, management of medication therapies, and urinary screening of risk factors. Quality improvement initiatives were proposed, such as online clinical pathways or selfmanagement interventions to support general practitioners in their care coordination role and involve patients in their own care. Conclusion: optimal CKD care relies on an interaction between self-management and primary care, supported by secondary and tertiary care. While quality improvement initiatives are necessary to optimize patient care and follow guidelines, a patient-centered, individualized, and preventive approach is crucial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".