Proceedings From a Canadian Nephrology Forum: Nephrology Is Back
Bibliographic record
Abstract
PURPOSE OF CONFERENCE: On January 18, 2020, the Nephrology is Back learning day forum was held in Toronto, ON, Canada. The objectives of the meeting were to describe recent advances in nephrology for community and academic nephrologists and patients, and to define challenges and opportunities for integration of new data into clinical practice. The intent was to test a unique forum for continuing medical education integrating physician and patient experiences with the goal of encouraging change in practice. SOURCES OF INFORMATION: Program content was based on current literature and clinical experience. Additional information was provided by patient partners who attended the meeting to provide their perspective on current issues in nephrology. METHODS: A steering committee (A.L., A.S., and D.S.) developed goals and an outline for the content to be covered over the course of the meeting and led the recruitment of speakers. Speakers were asked to develop their presentations independently following direction by the committee, based on primary sources, including their own experiences. Presentations were followed by discussion including both physicians and patients, and participants had an opportunity to evaluate the conference and its outcomes. KEY FINDINGS: We present a unique approach to providing continuing medical education by including both physicians and patients in the learning process. Patient perspectives accompanying presentations around data and other clinical topics provided a much different environment from other knowledge translation exercises. We believe this represents an innovative approach for knowledge translation that allows physicians to address clinical topics in a novel manner, including the integration of new findings into practice and the need to cascade this education to their peers. LIMITATIONS: Because the conference was a one-time event, it has been difficult to assess the actual clinical impact of the knowledge translation exercise and whether physician behaviors have changed as a result of the activity. The conference could also have included broader representation from across Canada. IMPLICATIONS: The success of this test forum among both physicians and patient partners suggests that the inclusion of patient partners in learning could have an important role in future educational initiatives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.152 | 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".