Structures for quality assurance and measurements for kidney replacement therapies: A multinational study from the <scp>ISN</scp>‐<scp>GKHA</scp>
Bibliographic record
Abstract
AIM: Optimal care for patients with kidney failure reduces the risks of adverse health outcomes, including cardiovascular events and death. We evaluated data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) to assess the capacity for quality service delivery for kidney failure care across countries and regions. METHOD: We explored the quality of kidney failure care delivery and the monitoring of quality indicators from data provided by an international survey of stakeholders from countries affiliated with the ISN from July to September 2022. RESULTS: One hundred and sixty seven countries participated in the survey, representing about 97.4% of the world's population. In countries where haemodialysis (HD) was available, 81% (n = 134) provided standard HD sessions (three times weekly for 3-4 h per session) to patients. Among countries with peritoneal dialysis (PD) services, 61% (n = 101) were able to provide standard PD care (3-4 exchanges per day). In high-income countries, 98% (n = 62) reported that >75% of centers regularly monitored dialysis water quality for bacteria compared to 28% (n = 5) of low-income countries (LICs). Capacity to monitor the administration of immunosuppression drugs was generally available in 21% (n = 4) of LICs, compared to 90% (n = 57) of high-income countries. There was significant variability between and within regions and country income groups in reporting the quality of services utilized for kidney replacement therapies. CONCLUSION: Quality assurance standards on diagnostic and treatment tools were variable and particularly infrequent in LICs. Standardization of delivered care is essential for improving outcomes for people with kidney failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".