Using the knowledge-to-action framework to guide the timing of dialysis initiation
Bibliographic record
Abstract
PURPOSE OF REVIEW: The optimal time at which to initiate chronic dialysis remains unknown. Using a contemporary knowledge translation approach (the knowledge-to-action framework), a pan-Canadian collaboration (CANN-NET) set out to study the scope of the problem, then develop and disseminate evidence-based guidelines addressing the timing of dialysis initiation. The purpose of this review is to summarize the key findings and describe the planned Canadian knowledge translation strategy for improving knowledge and practices pertaining to the timing dialysis initiation. RECENT FINDINGS: New research has provided considerable insights regarding the initiation of dialysis. A Canadian cohort study identified significant variation in the estimated glomerular filtration rate level at dialysis initiation, and a survey of providers identified related knowledge gaps that might be amenable to knowledge translation interventions. A recent knowledge synthesis/guideline concluded that early dialysis initiation is costly, and provides no measureable clinical benefits. A systematic knowledge translation intervention including a multifaceted approach may aid in reducing variation in practice and improving the quality of care. SUMMARY: Utilizing the knowledge-to-action framework, we identified practice variation and key barriers to the optimal timing for dialysis initiation that may be amenable to knowledge translation strategies.
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".