Pathology Training for Cancer Diagnosis in Africa
Bibliographic record
Abstract
OBJECTIVES: In response to requests for training in cancer pathology, two virtual training courses were organized: one in English for participants in Nigeria and another in French for participants in Francophone Africa. Each course had weekly 90-minute sessions covering essential topics in cancer pathology led by global experts. METHODS: Two research questions were investigated for both courses: (1) did the participants improve their knowledge of the topics covered during the course, and (2) did the course participants appreciate the virtual training format? RESULTS: The Nigeria course enrolled 85 participants from 26 Nigerian states; the Francophone Africa course enrolled 425 participants from 18 African countries. In the pre-post technical assessment, participants increased their scores on average by 3.4% (P > .05) in the Nigeria course and by 13.1% (P < .001) in the Francophone Africa course. On the postcourse survey, 95.8% of Nigerian respondents and 96.1% of Francophone African respondents reported being satisfied or very satisfied with the virtual format. CONCLUSIONS: Virtual training is a promising tool to improve cancer diagnosis in Africa, as the experience of the courses illustrates that participants appreciate the virtual format. Continued training is required to reinforce skills and enable participants to appropriately apply new knowledge to their daily practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".