An interview with Peter H. Buschang
Bibliographic record
Abstract
Dr. Peter Buschang is regent professor and director of orthodontic research. He has been at Texas A&M University Baylor College of Dentistry since 1988. Dr. Buschang received his PhD in 1980 from the University of Texas at Austin; he spent 3 years as a NIDR postdoctoral fellow at the University of Connecticut, and five years as a FRSQ scholar at the University of Montreal. Every year, Dr. Buschang teaches in 16 different courses, 7 of which he directs. In addition to more than 100 lecture hours per year, he spends hundreds of hours mentoring students. For his teaching efforts, Dr. Buschang was awarded the Robert E. Gaylord Award of Excellence in Orthodontic education in 1992, 1998, 2004, and 2010. He also gives 1-2 day evidence-based CE courses throughout the world. The residents he has taught recently honored him by pledging to fund the Peter H. Buschang Endowed Professorship of Orthodontics. His research interests pertain to craniofacial growth and assessment of treatment effects. Dr. Buschang has been funded regularly over the years by the Medical Research Council of Canada, Fonds de le Recherche en Santé du Québec, the NIH, and the American Association of Orthodontics Foundation. He has mentored over 140 Master's and PhD students, and 49 dental students. Dr. Buschang has published over 250 peer-reviewed articles, 15 book chapters and 198 abstracts. He has given over 150 invited talks and lectures in 14 different countries. For his work with the American Board of Orthodontics, Dr. Buschang was awarded the Earl E. and Wilma S. Shepard Award. Dr. Buschang is the only non-orthodontist ever to have been made an honorary member of both the American Association of Orthodontics (2005) and the Edward H. Angle Society of Orthodontics (2009), the two most prestigious orthodontic groups.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".