Choosing Wisely
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to contribute to the Choosing Wisely Canada campaign and develop a list of 5 items for nephrology health care professionals and patients to re-evaluate based on evidence that they are overused or misused. SOURCES OF INFORMATION: A working group was formed from the Canadian Society of Nephrology (CSN) Clinical Practice Guidelines Committee. This working group sequentially used a multistage Delphi method, a survey of CSN members, a modified Delphi process, and a comprehensive literature review to determine 10 candidate items representing potentially ineffective care in nephrology. An in-person vote by CSN members at their Annual General Meeting was used to rank each item based on their relevance to and potential impact on patients with kidney disease to derive the final 5 items on the list. KEY MESSAGES: One hundred thirty-four of 609 (22%) CSN members responded to the survey, from which the CSN working group identified 10 candidate-misused items. Sixty-five CSN members voted on the ranking of these items. The top 5 recommendations selected for the final list were (1) do not initiate erythropoiesis-stimulating agents in patients with chronic kidney disease (CKD) with hemoglobin levels greater than or equal to 100 g/L without symptoms of anemia; (2) do not prescribe nonsteroidal anti-inflammatory drugs for individuals with hypertension or heart failure or CKD of all causes, including diabetes; (3) do not prescribe angiotensin-converting-enzyme inhibitors in combination with angiotensin II receptor blockers for the treatment of hypertension, diabetic nephropathy or heart failure; (4) do not initiate chronic dialysis without ensuring a shared decision-making process between patients, their families, and their nephrology health care team; and (5) do not initiate dialysis in outpatients with CKD category G5-ND in the absence of clinical indications. LIMITATIONS: A low survey response rate of both community and academic nephrologists could contribute to sampling bias. However, the purpose of this report is to generate discussion, rather than study practice variation. IMPLICATIONS: These 5 evidence-based recommendations aim to improve outcomes and individualize care for patients with kidney disease, while reducing inefficiencies and preventing harm.
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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 | 0.053 | 0.275 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".