60 Optimizing overdiagnosis education for medical students
Bibliographic record
Abstract
Objectives Understanding overdiagnosis is difficult for healthcare workers, both in theory and in practice. At it’s core it turns people into patients needlessly by medicalizing everyday occurrences or by expanding disease definitions. For trainees, the terms ’false positive’ and ’misdiagnosis’ are commonly conflated with ’overdiagnosis.’ Although medical schools in Canada provide some instruction in the fundamentals of clinical epidemiology, overdiagnosis and overtreatment are typically ignored as objectives in undergraduate medical curriculum. For example, Toronto Notes is regarded as a comprehensive resource for students learning about medicine and preparing for the Medical Council of Canada Qualifying Examination (MCCQE), but the term ’overdiagnosis’ only appears three times in the textbook – only in connection with prostate-specific antigen screening. To increase students’ understanding of the concepts and demonstrate how to apply them in subsequent training and practice, this must be expanded and generalised, and instructional techniques must be developed. Method The major goals of this workshop will be to pinpoint the key components of overdiagnosis education and the best pedagogical strategies for imparting to undergraduate medical students the ideas and issues at the heart of overdiagnosis. Medical educators with expertise in teaching overdiagnosis will participate in the workshop together with medical students. Broader examples will be sought for teaching undergraduate medical students about the concepts and issues surrounding overdiagnosis. A list of topics and themes in overdiagnosis and overtreatment will be compiled to inform a curriculum on the subject. The session will present current methods and get feedback on what may be the best techniques to teaching medical students about overdiagnosis and preventing them from overdiagnosing as future physicians. Methods for disseminating information through problem-based learning or small-group discussions will be proposed. This will promote awareness and understanding of overdiagnosis and emphasize patient-centered care through collaborative decision-making. Results A list of themes in overdiagnosis and overtreatment will be compiled in order to inform a curriculum on the subject for medical students. Also, methods for disseminating information through problem-based learning or small-group discussions will be proposed. This will support both the promotion of awareness and understanding of overdiagnosis and an emphasis on patient-centered care through collaborative decision-making. The major goals of this workshop will be to pinpoint the key components of overdiagnosis education and the best pedagogical strategies for imparting to undergraduate medical students the ideas and issues at the heart of overdiagnosis. The deliberations and input from this workshop will inform a paper on the topic that will be submitted to a medical education journal. Conclusions The outcomes of this workshop can be used to enhance the CanMEDS 2025, which is currently being developed. CanMEDS is a framework that identifies and defines the skills doctors need to effectively address the patients‘ healthcare needs. Medical students will be better equipped to undertake good care, and identify substandard care if they are more aware of potential for overdiagnosis, and informed of the advantages and limitations of treatments and procedures. The deliberations and input from this workshop will inform a paper on the topic that will be submitted to a medical education journal.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.022 |
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".