Competency based medical education in nuclear cardiology: A tale of two axes
Bibliographic record
Abstract
BACKGROUND: Across medical specialties, including nuclear cardiology, competency based medical education (CBME) changes the emphasis of learning from a time or experiential emphasis to a proficiency focused approached. Plotted on a learning-curve graph the emphasis on learning has shifted from the duration/ volume-based x-axis to the performance-based y-axis. CURRENT STATUS: It has proven difficult to establish y-axis-based standards within nuclear cardiology to assess learning. As such there is a paucity of data to verify current experiential training targets and only recently is data emerging that seeks to find CBME targets by which proficiency (y-axis units) can be evaluated. Initial reports from such CBME-oriented studies indicate that in current nuclear cardiology practice, the number of studies required to achieve competency is dependent upon the chosen measure of competency that is assessed (summed stress score versus % LV ischemia), the case mix, and the modality being learnt (PET versus SPECT). Recent findings have also suggested that prior levels of experiential training may be an underestimation of the number of supervised studies learners need to interpret before they achieve competency. SUMMARY: Nuclear cardiology training has adopted the concept of CBME and is progressing toward a more modern approach to trainee assessment. This brief review provides the background, current requirements and insights into new developments in nuclear cardiology training.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".