Adherence to Choosing Wisely Africa recommendations in breast cancer care: a cross-sectional study across three Sub-Saharan African centres
Bibliographic record
Abstract
OBJECTIVE: The expenses associated with cancer treatment are increasing at a rapid pace. The financial strain of providing care is experienced worldwide, but is particularly pronounced in low and middle-income countries (LMICs). This has resulted in a growing acknowledgement of the importance of value-based cancer care. Choosing Wisely Africa (CWA) is an initiative aimed at reducing the excessive use and expenses associated with cancer treatment. In this study, we assessed adherence to CWA recommendations for the treatment of breast cancer in three high-volume cancer centres in Sub-Saharan Africa (SSA). DESIGN: tests, to compare adherence among these countries. SETTINGS: This study was conducted in three cancer centres (Ocean Road Cancer Institute, Rwanda Military Hospital and Korle Bu Teaching Hospital) in three countries (Tanzania, Rwanda and Ghana, respectively). PARTICIPANTS: A total of 542 patients were recruited. Eligible patients were those with a breast cancer diagnosis and complete data as pertaining to five CWA recommendations. RESULTS: A total of 542 participants with a mean age of 51 years were included. Participants were well distributed across Ghana (37%), Rwanda (34%) and Tanzania (29%). Female patients represented 97% of the study cohort. Half (51%) of the participants had some form of insurance. The study observed high adherence to cancer staging (94%) before treatment and histological confirmation (91%) before breast lump removal across all sites. Hypofractionation was used in 0% of cases in Rwanda, 42% in Ghana and 70% in Tanzania. CONCLUSION: This study provides critical insights into the implementation of CWA recommendations in breast cancer care in SSA. It highlights the disparities in adherence to CWA recommendations across different centres, showing the need for policy-driven changes and healthcare infrastructure improvement to standardise cancer care practices in LMICs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".