Choosing Wisely Canada Cancer List: Ten Low-Value or Harmful Practices That Should Be Avoided In Cancer Care
Bibliographic record
Abstract
PURPOSE: Choosing Wisely Canada, modeled after Choosing Wisely in the United States, is intended to identify low-value or potentially harmful practices relevant to the Canadian health care environment. Our objective was to use multidisciplinary, pan-Canadian, physician-based consensus to identify a list of low-value or harmful cancer practices frequently used in Canada. METHODS: A Task Force convened by the Canadian Partnership Against Cancer included physician representation from the Canadian Society of Surgical Oncology, Canadian Association of Medical Oncologists, and Canadian Association of Radiation Oncology, and an expert advisor. The methodology included four phases: identify potentially relevant items, develop a long list, refine and reduce the long list to a short list, and select and endorse a final list. A framework-driven consensus process and a series of electronic surveys and voting processes were used to capture consensus. RESULTS: Sixty-six potentially relevant cancer-related practices were identified. The long list (41 practices) was reduced to a short list of 19 practices. Of the 10 practices on the final list, five are completely new, and five are revisions or adaptations of practices from previous US society lists. Six of the 10 involve multiple disease sites, and four are disease-site specific. One relates to diagnosis, six relate to treatment, two relate to surveillance/survivorship, and one practice spans the cancer care continuum. CONCLUSION: The cancer list was developed in partnership with the Canadian Society of Surgical Oncology, Canadian Association of Medical Oncologists, and Canadian Association of Radiation Oncology. Using knowledge translation and exchange efforts, this list should empower patients with cancer and physicians to assist in a targeted conversation about the appropriateness and quality of individual patient care.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".