It Takes Time: Developing a Standardized Strategy to Improve Timely Modality Education and Home Dialysis Choice Rates in Patients with Advanced Kidney Disease in Toronto, Canada
Bibliographic record
Abstract
Background: Patients with progressive chronic kidney disease (CKD) should receive timely education that allows them to choose a treatment path that aligns with their care goals and lifestyle. Home-based dialysis modalities have been associated with increased quality of life and reduced health care costs. Modality education has been shown to increase rates of home dialysis as the initial dialysis strategy. Delays in timely education may reduce home dialysis choice rates. Barriers to timely modality education include lack of a standardized referral process, physician practice variation and patient preparedness to engage in discussion. The aim of this study was to assess whether the implementation of a standardized referral process could increase the rates of timely modality education and home dialysis choice. Methods: This was a quality improvement study performed at a single center in Toronto, Canada between 2019-2023. Patients with a 2-year Kidney Failure Risk Equation (KFRE2) of >= 40% were recommended for modality education as outlined by the provincial regulatory body. Rates of modality education and home dialysis choice were recorded on a quarterly basis both before and after implementation of a standardized referral process. Results: 1451 encounters were identified between 2019-2023. Prior to initiation of a standardized referral process, 647/1134 (57.1%) of eligible patients received modality education and 218/647 (33.7%) of educated patients choose home dialysis as their preferred modality. After initiation of a standardized referral process, 177/317 (55.8%) of eligible patients received modality education and 54/177 (30.5%) of patients choose home dialysis as their preferred modality. Conclusions: There was no significant change in modality education or home dialysis choice rates after initiation of a standardized strategy. Timing of education should not be restricted to arbitrary cut-off values but requires ongoing mentorship and support of patients at an early stage of disease course.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".