Personalising haemodialysis treatment with incremental dialysis for incident patients with end-stage kidney disease: an implementation study protocol
Bibliographic record
Abstract
INTRODUCTION: Incremental dialysis is a personalised dialysis prescription based on residual kidney function that allows for the initial use of shorter duration, less frequent and less intense dialysis. It has been associated with enhanced quality of life and decreased healthcare costs when compared with conventional dialysis. While nephrologists report prescribing incremental dialysis, few dialysis programmes offer a systematic approach in offering and evaluating its use. To move evidence into practice, and in order to improve the safety and quality of providing incremental dialysis care, we have designed an implementation study. This study aims to evaluate the systematic assessment of patients starting facility-based haemodialysis for eligibility for incremental dialysis, and the prescription and monitoring of incremental dialysis treatment. METHODS AND ANALYSIS: A hybrid effectiveness and implementation study design is being used to evaluate the implementation of the programme at dialysis sites in Alberta, Canada. The Reach, Effectiveness, Adoption, Implementation and Maintenance framework will be used to capture individual-level and organisational-level impact of the project. Clinical outcomes related to kidney function will be monitored on an ongoing basis, and patient-reported outcomes and experience measures will be collected at baseline and then quarterly throughout the first year of dialysis. ETHICS AND DISSEMINATION: The study was approved by the Health Research Ethics Board of the University of Alberta. The study is funded by the Strategic Clinical Networks of Alberta Health Services. The study will help answer important questions on the effectiveness of incremental dialysis, and inform the acceptability, adoption, feasibility, reach and sustainability of incremental dialysis within provision of haemodialysis care.
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 | 0.067 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.012 |
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".