Chronic kidney disease and <scp>value‐based</scp> care: Lessons from innovation, iteration, and ideation in primary care
Bibliographic record
Abstract
Value-based primary care has reduced health care costs, improved the quality of rendered care, and enhanced the patient experience. Value-based care emphasizes prevention, outreach, follow-up, patient engagement, and comprehensive, whole-person health. Primary care Accountable Care Organizations have leveraged technology-enabled workflows, practice transformation, and cutting-edge data and analytics to achieve success. These efforts are increasingly aided by predictive modeling used in the context of patient identification and prioritization algorithms. Value-based kidney care programs can glean salient takeaways from successful value-based primary care methods and models. The kidney care community is experiencing unprecedented transformation as novel payer programs and financial models burgeon. The authors contend these efforts can be accelerated by the adoption of techniques honed in value-based primary care. To optimize value-based kidney care, though, nephrology thought leaders must transcend the archetype of value-based primary care. To do so, the nephrology community must: (1) impel behavioral change among fee-for-service adherents; (2) harness emerging policy, guidelines, and quality measures; (3) adopt innovative tools, technologies, and therapies. In aggregating lessons from value-based primary care-and leveraging novel methodologies and approaches-the kidney care community will be better equipped to achieve the quadruple aim for kidney care.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".