Bibliographic record
Abstract
Sir: I appreciated the article “Evidence-Based Education in Plastic Surgery” by Drs. Johnson, Chung, and Waljee published in the August of 2015 issue of the Journal.1 The summary of major events in the development of graduate medical education along with the timeline shown in Figure 1 is helpful as we contend with our current challenges in the effort to better serve our trainees and our patients. Certainly, all agree that study and evidence should guide education reform. In the section entitled Plastic Surgery: The Need for Evidence-Based Education, page 262e, with regard to the American Council for Graduate Medical Education milestones, the question was raised, “How were they chosen?” I refer the authors and readers to Dr. Mary H. McGrath’s article, The Plastic Surgery Milestone Project.”2 Dr. McGrath described the thoughtful process by which the current version of the plastic surgery milestones was developed. Significant input and review by key stakeholders in our specialty was obtained at multiple steps in the process. There was strong consensus of program directors who were involved in the pilot trial that the milestones provide a very useful framework for plastic surgery graduate medical education. We all agree that there is urgency for better tools for competency assessment and that the current version of the milestones should be reviewed and likely revised as we gain more experience with their use. A major current focus of the American Council of Academic Plastic Surgeons is to explore how competency-based education can be used to improve training. Our members are actively developing new tools for competency-based instruction and assessment. John Potts, M.D., senior vice president of the American Council for Graduate Medical Education, participated on a panel at the American Association of Plastic Surgeons this past spring entitled, “Competency versus Time Based Residency Training in Plastic Surgery.” A proposal was presented by Joseph Losee, M.D., and Vu Nguyen, M.D., at the University of Pittsburgh for the first competency-based program in the United States. We were fortunate to have Peter Ferguson, M.D., share his experience as an early adopter of competency-based education from the University of Toronto Department of Orthopedic Surgery. The possibilities, pitfalls, and current challenges were reviewed. Dr. Potts was very supportive of thoughtful innovation in competency-based education but was clear about his reluctance to support milestones as major indicators of performance because experience with them is still very limited. There is currently a healthy spirit of collaboration among the American Council for Graduate Medical Education, the American Board of Plastic Surgery, the American Council of Academic Plastic Surgeons, and our specialty societies to work together to provide the best education for the next generation of plastic surgeons. I encourage the authors and readers committed to plastic surgery education to become members of the American Council of Academic Plastic Surgeons. Although our membership includes plastic surgery residency program directors and chairs, membership is open to all who are actively engaged in teaching in accredited programs in the United States. Each year, the American Council of Academic Plastic Surgeons sponsors a grant with the Plastic Surgery Foundation for research in plastic surgery education. An American Council of Academic Plastic Surgeons retreat is planned for February 6 and 7, 2016, in Chicago. A significant component of the retreat will be related to evidence-based education, assessment, and faculty development. I welcome all committed to graduate medical education in plastic surgery to become American Council of Academic Plastic Surgeons members and attend. DISCLOSURE The author has no financial interest to declare in relation to the content of this communication. W. John Kitzmiller, M.D. Section of Plastic and Burn Surgery University of Cincinnati College of Medicine 231 Albert Sabin Way Cincinnati, Ohio 45267 [email protected]
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 | 0.026 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".