Cologne Consensus Conference A Meeting of the International Academy for CPD Accreditation A World Apart: We Are Together
Bibliographic record
Abstract
The ninth annual Cologne Consensus Conference was held virtually on 10–11 September 2020. The two-day educational event was organised by the International Academy for CPD Accreditation (the Academy), a network of colleagues dedicated to promoting and enhancing continuing professional development (CPD) accreditation systems throughout the world. This year’s conference was hosted by the Accreditation Council for Continuing Medical Education (ACCME) and once again planned in cooperation with the European Cardiology Section Foundation (ECSF) and the Royal College of Physicians and Surgeons of Canada. The conference’s ninth iteration was originally slated to be a live meeting taking place in Chicago, Illinois, USA (home to the ACCME offices), but was moved to a fully online format due to the ongoing COVID-19 pandemic. Appropriately, the programme’s theme was A World Apart: We Are Together and focused on the continued alignment of global accreditation standards and increasing international collaborations. This conference report summarises the meeting content and discussions, including a description and formal adoption of the final Standards for Substantive Equivalency between Continuing Professional Development/Continuing Medical Education (CPD/CME) Accreditation Systems.
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".