Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis
Bibliographic record
Abstract
Unplanned kidney dialysis is associated with higher morbidity and mortality rates among advanced chronic kidney disease patients.The incidence of unplanned dialysis can be attributed to myriad factors, but importantly, many of them are modifiable with appropriately timed intervention and treatment planning.A system tailored to the clinical question of the optimal dialysis preparation timeline could be crucial in mitigating the risk factors associated with initiating dialysis in an unplanned manner.Hereinafter, the development of clinical machine learning models for the prediction of kidney failure over short timeframes of 6 and 12 months is studied.The groundwork for the machine learning analysis is laid out, covering the characterization of The Ottawa Hospital's Multi Care Kidney Clinic dataset, the data processing, and a comparison of machine learning to traditional methods.We find that a data-driven approach proffers an opportunity to significantly reduce the burden of unplanned dialysis in advanced CKD centers. List of Abbreviations uACR: urine albumin-to-creatinine ratioAUC-ROC: area under the receiver operating characteristic curve AUC-PR: area under the precision-recall curve CI: confidence interval CKD: chronic kidney disease eGFR: estimated GFR ESKD: end stage kidney disease GFR: glomerular filtration rate
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".