Creation and pilot testing of cases for case-based learning: A pedagogical approach for pathology cancer diagnosis
Bibliographic record
Abstract
BACKGROUND: Case-based learning (CBL) is an established pedagogical active learning method used in various disciplines and defined based on the field of study and type of case. The utility of CBL for teaching specific aspects of cancer diagnosis to practising pathologists has not been previously studied in sub-Saharan Africa. OBJECTIVES: We aimed to pilot test standardised cancer cases on a group of practising pathologists in sub-Saharan Africa to evaluate case content, clarity of questions and delivery of content. METHODS: Expert faculty created cases for the four most commonly diagnosed cancers. The format included mini-cases and bullet cases which were all open-ended. The questions dealt with interpretation of clinical information, gross specimen examination, morphologic characteristics of tumours, ancillary testing, reporting and appropriate communication to clinicians. RESULTS: Cases on breast, cervical, prostate and colorectal cancers were tested on seven practising pathologists. Each case took an average of 45-90 min to complete.Questions that were particularly challenging to testers were on: Specimens they should have been but for some reason were not exposed to in routine practice.Ancillary testing and appropriate tumour staging.New knowledge gained included tumour grading and assessment of radial margins. Revisions to cases were made based on testers' feedback, which included rewording of questions to reduce ambiguity and adding of tables to clarify concepts. CONCLUSION: Cases were created for CBL in Kenya, but these are applicable elsewhere in Africa and beyond to teach cancer diagnosis. The pilot testing of cases prepared faculty for the actual CBL course and feedback provided by the testers assisted in improving the questions and impact on day-to-day 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 | 0.027 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".