Kidney Disease: Improving Global Outcomes Summit Recommendations on Implementation of Diabetes Management in CKD: From Primary to Data-Driven Collaborative Care
Bibliographic record
Abstract
Type 2 diabetes and chronic kidney disease (CKD) are preventable and treatable. Their silent and progressive clinical course calls for structured assessment with timely feedback to patients and care providers for activating decision-making. Apart from CKD, patients with diabetes can have complications affecting multiple organs, notably the cardiovascular system, eyes, and feet. International practice guidelines recommend annual assessment of the eyes, feet, blood, and urine to detect silent complications and measure cardiovascular-kidney-metabolic (CKM) risk factors to ensure early intervention, including treatment to multiple targets and use of organ-protective drugs. In this report, we highlight the barriers and gaps in the implementation of practice guidelines in managing diabetes in CKD with proposed solutions to overcome such barriers. By improving the practice environment and workflow, nurses can be trained to perform protocol-guided evaluation under medical supervision. The systematic data collection enables physicians to make timely decisions, including drug prescriptions and referrals to other specialists to promote collaborative care, whereas nurses can use the personalized data to empower patient self-management and improve health literacy. This ongoing data collection will form a register to align payers, providers, and patients in delivering data-driven and value-based care with the creation of real-world evidence to verify treatment effectiveness and identify care gaps while providing on-the-job training. When accompanied by a biobank, the ongoing collection and analysis of this multidimensional data will refine diagnosis, classification, prognosis, and treatment in pursuit of precision medicine.
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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 | 0.097 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.013 | 0.034 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".