Kidney Failure Risk Equation in vascular access planning: a population-based study supporting value in decision making
Bibliographic record
Abstract
Background: The Kidney Failure Risk Equation (KFRE) can play a better role in vascular access (VA) planning in patients with chronic kidney disease (CKD) requiring hemodialysis (HD). We described the VA creation and utilization pattern under existing estimated glomerular filtration rate (eGFR)-based referral, and investigated the utility of KFRE score as an adjunct variable in VA planning. Methods: who chose HD as dialysis modality from January 2010 to August 2020 were included from a population-based database in British Columbia, Canada. Modality selection date was the index date. Exposures were categorized as (i) current eGFR-based referral, (ii) eGFR-based referral plus KRFE 2-year risk score on index date (KFRE-2) >40% and (iii) eGFR-based referral plus KFRE-2 ≤40%. We estimated the proportion of patients who started HD on arteriovenous fistula/graft (AVF/G) within 2 years, indicating timely pre-emptive creation, and the proportion of patients in whom AVF/G was created but did not start HD within 2 years, indicating too-early creation. Results: -value <.001). Conclusions: KFRE in addition to existing eGFR-based referral for VA creation has the potential to improve VA resource utilization by ensuring more patients start HD on AVF/G and may minimize too-early/unnecessary creation. Prospective research is necessary to validate these findings.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".