The CSN COVID-19 Rapid Response Program
Bibliographic record
Abstract
The coronavirus disease (COVID-19) pandemic has created unprecedented challenges in caring for individuals living with kidney disease. In response to a growing call for up-to-date information and evidence-informed advice, the Canadian Society of Nephrology has established a COVID-19 Rapid Response Team that will leverage existing evidence and national expertise to inform kidney care practices in the COVID-19 era. Given limited published evidence and compressed timelines, formal clinical practice guidelines are not feasible, and we have adopted rapid review methods to instead provide interim guidance across identified priority areas. In this article, we describe the methodological approach that was applied in developing a first iteration of guidance documents addressing clinical and operational aspects of care for patients treated with in-center hemodialysis, home dialysis, those with advanced chronic kidney disease, those with glomerulonephritis, and those with acute kidney injury. We further describe strategies for maintaining ongoing engagement with the renal community to elicit emerging needs and perspectives as the situation unfolds.
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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 | 0.137 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".