Cologne Consensus Conference Standards and Guidelines in Accredited CPD September 13-14, 2019, Cologne, Germany
Bibliographic record
Abstract
On September 13–14, 2019 the eighth annual Cologne Consensus Conference was held in Cologne, Germany. The two-day educational event was organised by the International Academy of CPD Accreditors, a network of colleagues dedicated to promoting and enhancing continuing professional development (CPD) accreditation systems throughout the world. The conference was planned in cooperation with an impressive group of organisations representing leading European and North American institutions: the European Cardiology Section Foundation (ECSF), the Accreditation Council for CME (ACCME), the Royal College of Physicians and Surgeons of Canada, and Continuing Medical Education–European Accreditors (CME-EA). For the conference’s eighth iteration, Standards and Guidelines in Accredited CPD was chosen as the program topic and educational focus; a choice reflecting increasing international collaborations and an evolution towards consistency and standards across global accreditation systems. A specific list of domains and criteria (developed under a broader initiative already underway by the Academy) would serve as the core content around which the conference was planned. This conference report describes the initiative, the proposed standards to date, highlights of the Cologne Consensus Conference discussions and feedback, and the ongoing process of achieving consensus on the standards yet to be finalised.
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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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 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".