American Society for Surgery of the Hand (ASSH) Presidential Address Themes, 1964–2018: Revisiting Our History as We Move Forward
Bibliographic record
Abstract
The American Society for Surgery of the Hand (ASSH) was established in 1946. Since then, important advances have been made in the diagnosis and treatment of conditions affecting the upper extremity. However, there has been little documentation regarding how the largest and oldest society dedicated to hand surgery has evolved over time. Furthermore, an understanding of the history of the ASSH and the specialty of hand surgery should be emphasized in resident and fellow education. The authors aim to provide a historical overview of the ASSH through the speeches of ASSH past presidents that sheds light on future directions and long-term goals. Presidential addresses from 1961 to 2018 (courtesy of ASSH Chase Library historical archives) were reviewed. The overall percentage of ASSH presidents by specialty was 67% orthopedic, 25% plastic surgery, and 8% general surgery. The most common speech theme overall was how to be a good hand surgeon (31%). The most common speech themes were, by decade: the 1960s, history and the current state of ASSH; the 1970s and 1980s, assessments of how to be a good surgeon and goals for ASSH; the 1990s, health care and governmental regulation; the 2000s, how to be a better hand surgeon; and the 2010s goals for ASSH. In earlier years, there was more of a focus on education and technical skill development in the ASSH. Work-life balance, introduced in the 1990s, has become more of a focus in the past 20 years. Revisiting the history of the ASSH and its goals allows us to reflect on progress made while recognizing what is important as we look into the future. Furthermore, as we strive to make progress in the field of hand surgery during the current pandemic, valuable tools surface that will allow the specialty to strengthen its education, research, and patient care delivery in the future.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".