A New List for Choosing Wisely Canada From the “Hidden Profession” of Medical Laboratory Science
Bibliographic record
Abstract
OBJECTIVES: Choosing Wisely Canada (CWC) publishes practices that may contribute to medical overuse and patient harm. Many practices concern laboratory testing, but the recommendations are often written for the test-ordering professionals. Our objective was to develop a list for CWC reflecting the scope of practice of nonpathologist medical laboratory professionals (MLPs). METHODS: We used a national survey, a convention session, and a panel of MLPs from across Canada to generate content for the CWC list. We used a modified Delphi process to identify the most important items and scoping reviews to gather evidence supporting each item. RESULTS: We identified 95 potential CWC list items. After 2 Delphi rounds, there was little movement in the top items. Scoping reviews revealed varying degrees of evidentiary support, which influenced the composition of the final list of 7 CWC items submitted. Three of the final recommendations address ways MLPs preserve the status quo with respect to overutilization of laboratory tests by other health care professionals. The remaining recommendations prompt MLPs to exert clinical judgment in specific scenarios, particularly where they can impact blood collection volumes. CONCLUSIONS: This work brings a more nuanced and comprehensive understanding of the relationships among MLPs, patient safety, and resource waste.
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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 | 0.064 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".