Twelve tips for adapting grand rounds for contemporary demands
Bibliographic record
Abstract
Grand rounds have evolved since inception in the early 1900s and subsequently, there has been a continual debate surrounding the best purpose for the time-honoured event. However, the purpose of grand rounds can be broadened to meet the needs of the medical community today, especially at a time where there is great distribution of medical practitioners and learners geographically and events such as COVID-19, which prevent the community from physically gathering. Using the evidence and lessons available from the literature we developed a grand rounds series with goals and objectives suited to our context. In this guide we provide twelve tips covering goal planning, logistics, presentation preparation and presentation delivery in order to illustrate how one could organize an informed grand rounds which is successful contextually.
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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 | 0.047 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".