Choosing Wisely Africa: Ten Low-Value or Harmful Practices That Should Be Avoided in Cancer Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Choosing Wisely Africa (CWA) builds on Choosing Wisely (CW) in the United States, Canada, and India and aims to identify low-value, unnecessary, or harmful cancer practices that are frequently used on the African continent. The aim of this work was to use physicians and patient advocates to identify a short list of low-value practices that are frequently used in African low- and middle-income countries. METHODS: The CWA Task Force was convened by the African Organization for Research and Training in Cancer and included representatives from surgical, medical, and radiation oncology, the private and public sectors, and patient advocacy groups. Consensus was built through a modified Delphi process, shortening a long list of practices to a short list, and then to a final list. A voting threshold of ≥ 60% was used to include an individual practice on the short list. A consensus was reached after a series of teleconferences and voting processes. RESULTS: Of the 10 practices on the final list, one is a new suggestion and 9 are revisions or adaptations of practices from previous CW campaign lists. One item relates to palliative care, 8 concern treatment, and one relates to surveillance. CONCLUSION: The CWA initiative has identified 10 low-value, common interventions in Africa's cancer practice. The success of this campaign will be measured by how the recommendations are implemented across sub-Saharan Africa and whether this improves the delivery of high-quality cancer care.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it