American Society of Maxillofacial Surgeons 2006 to 2016: Another Decade of Excellence in Education and Research
Bibliographic record
Abstract
Over the past 10 years, the American Society of Maxillofacial Surgeons (ASMS) has continued to advance to meet its mission of being the premier organization to represent maxillofacial and pediatric plastic surgery in the United States. These advances are focused on education of its members, to include the American Society of Maxillofacial Surgeons basic course, the preconference symposium, the annual meeting, two basic maxillofacial courses per year, advanced maxillofacial courses, a boot camp for craniofacial fellows, a cleft course, quarterly webinars, sponsored fellowships, a visiting professorship, and the ASMS journal. In addition, the ASMS has continued to advance as the premier national organization representing maxillofacial and pediatric plastic surgery in the United States, thereby positioning the organization as a primary advocate for these surgical specialties. Outreach of the ASMS has grown over the past decade and now includes representatives to the American Society of Plastic Surgeons/Plastic Surgery Foundation, the American Board of Plastic Surgeons, the American Medical Association, and most recently a seat as a governor with the American College of Surgeons. The ASMS has also initiated an annual Summer Leadership Seminar to explore topics of relevance in a changing health care environment. The present report outlines the major initiatives of the ASMS over the past 10 years.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".