Bibliographic record
Abstract
End-stage renal failure (ESRF) is a major cause of morbidity and mortality in North America as well as in the rest of the world. In addition, the treatment of patients who have ESRF is quite expensive. It is estimated that for an in-hospital hemodialysis patient, the average annual cost is approximately $54,000 in Canadian dollars and a peritoneal dialysis patient's annual care costs approximately $32,000 in Canadian dollars, whereas a kidney transplant patient costs about $25,000 dollars a year in Canadian dollars. And as renal replacement therapy by hemodialysis, periltoneal dialysis or successful kidney transplantation prolongs survival in ESRF patients, new dialysis patients have been outnumbering those who die from ESRF. To complicate matters further, the number of ESRF patients have been on the rise the last two decades and is expected to continue to rise at a rate of about 6 to 10% per year through the next one to two decades, putting more strain on the national economy, human resources and on an already strained health care system. Therefore, it makes sense to slow down or delay the progression of chronic renal parenchymal disease, or when possible, to prevent it from happening altogether. In this article, we will review the mechanisms and course of progression of renal injury and elaborate on the currently recommended strategies to delay or halt the progression of chronic renal disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".