Centralising curriculum feedback from graduates of small programmes
Bibliographic record
Abstract
Evaluation of results and impactThe course is evaluated with residents taking a pre-and post-test that consists of the same unknown EKGs.These 10 (very difficult) EKGs highlight 18 key knowledge points.Thus far our preliminary results show that: 19 residents took the pre-test and scored a mean of 8.16 interpretation points correct; 12 residents took the post-test and scored a mean of 12.75 knowledge points; and 16 residents gave a mean score of 4.75 ⁄ 5 for class satisfaction and educational importance.Our EKG curriculum is successful at teaching basic skills in EKGs to a group of highly motivated medical residents at a single institution.Residents show improvement of skills in the basic EKG skills domain as mandated by the AHA and ABIM.This curriculum is exportable, reproducible and does not require a cardiologist to teach.Further evaluation will demonstrate the areas of strength and weakness of our curriculum.Of note, increased cross-coverage due to new float demands in response to resident workhours changes limited participation in our class and may have affected outcomes.
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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".