Improving precision in prediction: Using kidney failure risk equations as a potential adjunct to vascular access planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The timing of referral for creation of vascular access in a patient with declining kidney function is difficult to predict. Current methods may result in patients undergoing unnecessary procedures and subsequent interventions on accesses that are never used. Multiple variables, including time for assessment, surgery and follow-up that considers the likelihood of access failure, and the estimated rate of kidney function decline, make vascular access planning challenging and difficult to balance. Better prediction tools that incorporate the risks of progressive decline in kidney function with the risk of access failure and the competing risk of death would facilitate decision-making in vascular access. The kidney failure risk equation is a validated, simple online tool that estimates the probability of the 2- and 5-year risk of reaching end-stage kidney disease. While the use of the kidney failure risk equation has not been validated as an adjunct to planning vascular access, it has potential and may facilitate more individualised care and more appropriate allocation of resources.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it