Evidence for the Prevention and Treatment of Stroke in Dialysis Patients
Bibliographic record
Abstract
The risks of both ischemic and hemorrhagic stroke are particularly high in dialysis patients of any age and outcomes are poor. It is therefore important to identify strategies that safely minimize stroke risk in this population. Observational studies have been unable to clarify the relative importance of traditional stroke risk factors such as blood pressure and cholesterol in those on dialysis, and are affected by biases that usually make them an inappropriate source of data on which to base therapeutic decisions. Well-conducted randomized trials are not susceptible to such biases and can reliably investigate the causal nature of the association between a potential risk factor and the outcome of interest. However, dialysis patients have been under-represented in the cardiovascular trials which have proven net benefit of commonly used preventative treatments (e.g., antihypertensive treatments, low-dose aspirin, carotid revascularization, and thromboprophylaxis for atrial fibrillation), and there remains uncertainty about safety and efficacy of many of these treatments in this high-risk population. Moreover, the efficacy of renal-specific therapies that might reduce cardiovascular risk, such as modulators of mineral and bone disorder, online hemodiafiltration, and daily (nocturnal) hemodialysis, have not been tested in adequately powered trials. Recent trials have also demonstrated how widespread current practices could be causing stroke. Therefore, it is important that reliable information on the prevention and treatment of stroke (and other cardiovascular disease) in dialysis patients is generated by performing large-scale randomized trials of many current and future treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".