The rationale for the need to study sodium-glucose co-transport 2 inhibitor usage in peritoneal dialysis patients
Bibliographic record
Abstract
. However, dialysis patients are at the highest risk of cardiovascular disease and would benefit most from effective cardioprotective therapies. There is emerging evidence from experimental studies and post hoc analyses of randomised clinical trials that SGLT2 inhibitors are well tolerated and may also be effective in preventing cardiovascular and mortality outcomes in patients with severe chronic kidney disease, including patients receiving dialysis. As such, extending the usage of SGLT2 inhibitors to dialysis patients could provide a major advancement in their care. Peritoneal dialysis (PD) patients have an additional unmet need for effective pharmacotherapy to preserve their residual kidney function (RKF), with its associated mortality benefits, and for treatment options that help reduce the risk of transfer to haemodialysis. Experimental data suggest that SGLT2 inhibitors, via various mechanisms, may preserve RKF and protect the peritoneal membrane. There is sound physiological rationale and an urgent clinical need to execute robust randomised control trials to study the use of SGLT2 inhibitors in PD patients to answer important questions of relevance to patients and healthcare systems.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".