Clinical Trials Targeting Recovery and Postdischarge Care in Dialysis for Acute Kidney Injury
Bibliographic record
Abstract
Receipt of dialysis for acute kidney injury is common and increasing. For patients still actively receiving dialysis, it is possible that the dialysis procedure itself decreases the likelihood of kidney recovery or confounds recognition that recovery has occurred. Accordingly, 2 ongoing trials are testing hypotheses that dialysis prescriptions to minimize dialysis-induced ischemia and/or standardize dialysis discontinuation will increase the likelihood of renal recovery compared to usual care. These will be some of the first clinical trials to focus on hospitalized patients during the recovery phase of their acute illness. Meanwhile, clinical trials in the postdischarge population have found that less than 30% of patients choose to enroll when interventions require in-person nephrology follow-up, suggesting more flexible and pragmatic follow-up pathways are needed. Key considerations for future trials in dialysis for acute kidney injury will include recruiting patients at the right time in their clinical course during the window between acute kidney injury development and recovery or death, as well as providing interventions/follow-up over great distances and in multiple care settings. Testing different care strategies in this rigorous manner may eventually help reduce variation in care across centers and identify evidence-based practices that promote kidney recovery in dialysis for acute kidney injury.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".