Choosing Wisely Africa: Insights from the front-lines of clinical care.
Bibliographic record
Abstract
e18835 Background: A multidisciplinary Task Force of African oncologists and patient representatives published the Choosing Wisely Africa (CWA) recommendations in 2020. These recommendations identified low-value, unnecessary, or harmful practices that are frequently used in Sub-Saharan Africa (SSA). We describe agreement and concordance with the recommendations from front-line oncologists across SSA. Methods: A self-administered electronic survey was distributed to members of the African Organization for Research & Training in Cancer and oncology groups within SSA using a hierarchical snowball method. The survey captured information about awareness of CWA, agreement with recommendations, and concordance with clinical practice. Descriptive statistics were used to summarize study results. Results: 49 individuals responded to the survey; 61% (30/49) were female and 59% (29/49) were clinical oncologists. Respondents represented 14 countries in SSA; 69% (34/49) practiced exclusively in the public system. Only 43% (21/49) were aware of the CWA list and 90% (44/49) agreed it would be helpful if the list was displayed in their clinic. There was generally high agreement (Table) with the recommendations (range 84-98%); highest agreement related to staging/defining treatment intent (98%). The proportion of oncologists who implemented these recommendations in routine practice was somewhat lower (range 68-100%). Lowest rates of concordance related to: use of shorter schedules of radiotherapy (68%). Conclusions: While most frontline SSA oncologists agree with CWA recommendations, efforts are needed to disseminate the list. Agreement with the recommendations is high but there are gaps in implementation in routine practice. Further work is required to understand barriers and enablers of implementation.[Table: see text]
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".