Highlights from the Second Choosing Wisely Africa conference: a roadmap to value-based cancer care in East Africa (9–10 February 2023, Dar es Salaam, Tanzania)
Bibliographic record
Abstract
The ecancer Choosing Wisely conference was held for the second time in Africa in Dar es Salaam, Tanzania, from the 9th to 10th of February 2023. ecancer in collaboration with the Tanzania Oncology Society organised this conference which was attended by more than 150 local and international delegates. During the 2 days of the conference, more than ten speakers from different specialties in the field of oncology gave insights into Choosing Wisely in oncology. Topics from all fields linked to cancer care such as radiation oncology, medical oncology, prevention, oncological surgery, palliative care, patient advocacy, pathology, radiology, clinical trials, research and training were presented to share and bring awareness to professionals in oncology, on how to choose wisely in their approach to their daily practice, based on the available resources, while trying to offer the maximum benefit to the patient. This report, therefore, shares the highlights of this conference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".