Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients reaching end-stage kidney disease face difficult decisions, including choosing among renal replacement therapies (RRTs). With an increasingly elderly and frail population, there is growing interest in conservative kidney management (CKM) as a viable alternative to dialysis. Shared decision-making (SDM) is a patient-centered approach to these decisions, in which choices are viewed within the explicitly discussed values and preferences of the patient. Patient decision aids (PDAs) are tools designed to facilitate these discussions. The choice between dialysis and CKM is particularly complex, given the poor prognostication data for CKM. This is an emerging area for PDAs in nephrology. This review highlights care gaps around SDM for dialysis versus CKM, presents current PDAs for making choices about RRTs and CKM and discusses exciting new work around the development of novel PDAs. RECENT FINDINGS: Many PDAs have been created recently, primarily to help with decisions about RRTs. Three new PDAs are in testing phases to aid with the more complex decision of choosing between dialysis and CKM. SUMMARY: International nephrology communities are moving toward improved SDM with their patients and PDAs are being developed to facilitate this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".