The impact of the Starting dialysis on Time, At home on the Right Therapy (START) project on the use of peritoneal dialysis
Bibliographic record
Abstract
Background:Peritoneal dialysis (PD) is actively promoted, but increasing PD utilisation is difficult. The objective of this study was to determine if the Starting dialysis on Time, At Home, on the Right Therapy (START) project was associated with an increase in the proportion of dialysis patients receiving PD within 6 months of starting therapy.Methods:Consecutive patients over age 18, with end-stage kidney failure, who started dialysis between 1 April 2015 and 31 March 2018 in the province of Alberta, Canada. Programmes were provided with high-quality data about the individual steps in the process of care that drive PD utilisation that were used to identify problem areas, design and implement interventions to address them, and then evaluate whether those interventions had impact. The primary outcome was the proportion of patients receiving PD within 6 months of starting dialysis. Secondary outcomes included hospitalisation, death or probability of transfer to haemodialysis (HD). Interrupted time series methodology was used to evaluate the impact of the quality improvement initiative on the primary and secondary outcomes.Results:A total of 1962 patients started dialysis during the study period. Twenty-seven per cent of incident patients received PD at baseline, and there was a 5.4% (95% confidence interval: 1.5–9.2) increase in the use of PD in the province immediately after implementation. There were no changes in the rates of hospitalisation, death or probability of transfer to HD after the introduction of START.Conclusions:The approach used in the START project was associated with an increase in the use of PD in a setting with high baseline utilisation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".