Overcoming Renalism: A Roadmap Towards Equitable Kidney Care
Bibliographic record
Abstract
Renalism, the exclusion of CKD patients from diagnostic and therapeutic interventions due to concerns about kidney injury, remains a barrier to equitable healthcare. Originally identified in cardiovascular disease (CVD) care, renalism extends to hypertension management, oncology, critical care, and emerging treatments such as COVID-19 therapies. Despite their elevated cardiovascular mortality risk, CKD patients are underrepresented in clinical trials and often denied life-saving interventions like percutaneous coronary interventions (PCIs) and intensive blood pressure management due to concerns about AKI, often overestimated without proper risk stratification. Both industry-sponsored and investigator-initiated trials frequently exclude CKD patients, especially those with advanced disease or on dialysis, due to challenges such as higher adverse event rates, increased mortality risk, difficulty demonstrating treatment benefits, and logistical burdens. Concerns over nephrotoxicity, drug dosing, and necessary dose adjustments further complicate their inclusion. This exclusion limits evidence-based treatment options, reinforcing disparities in care and compromising health equity, a fundamental pillar of the United Nations’ Sustainable Development Goals (SDGs). Addressing renalism requires a collaborative effort from clinicians, researchers, regulatory agencies, and the pharmaceutical industry. Expanding CKD representation in clinical trials, spanning critical care, oncology, and emerging therapies, will help counteract therapeutic nihilism and improve patient outcomes. By fostering inclusivity in research and clinical decision-making, the medical community can move toward universal health equity and optimized care for all, including the vulnerable CKD population.
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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 | 0.040 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".