Characteristics of highly successful orthopedic surgeons: a survey of orthopedic chairs and editors
Bibliographic record
Abstract
BACKGROUND: Highly successful orthopedic surgeons are a small group of individuals who exert a large influence on the orthopedic field. However, the characteristics of these leaders have not been well-described or studied. METHODS: Orthopedic surgeons who are departmental chairs, journal editors, editorial board members of the Journal of Bone and Joint Surgery (British edition), or current or past presidents of major orthopedic associations were invited to complete a survey designed to provide insight into their motivations, academic backgrounds and accomplishments, emotional and physical health, and job satisfaction. RESULTS: In all, 152 surgeons completed the questionnaire. We identified several characteristics of highly successful surgeons. Many have contributed prolific numbers of publications and book chapters and obtained considerable funding for research. They were often motivated by a "desire for personal development (interesting challenge, new opportunities)," whereas "relocating to a new institution, financial gain, or lack of alternative candidates" played little to no role in their decisions to take positions of leadership. Most respondents were happy with their specialty choice despite long hours and high levels of stress. Despite challenges to their time, successful orthopedic surgeons made a strong effort to maintain their health; compared with other physicians, they exercise more, are more likely to have a primary care physician and feel better physically. CONCLUSION: Departmental chairs, journal editors and presidents of orthopedic associations cope with considerable demands of clinical, administrative, educational and research duties while maintaining a high level of health, happiness and job satisfaction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".