The Training and Careers of Regional Anesthesia Fellows—1983–2002
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The education and subsequent careers of regional anesthesia fellows have not been examined but may provide insight into improving future fellowship training and/or the future of the subspecialty. METHODS: Regional anesthesia fellows educated during a 20-year period (1983-2002) were asked to complete a comprehensive survey that detailed their training, current professional setting, and use of regional anesthesia, and how they foresee the future of regional anesthesia. A separate survey of academic anesthesiology chairs assessed the role of and need for regional anesthesiologists in teaching departments. RESULTS: Twelve regional anesthesia fellowship programs in the United States and Canada provided contact information on 176 former fellows. The survey response rate from those practicing in North America was 49% (77/156). Two of the 12 responding institutions have trained 68% of regional anesthesia fellows. Of respondents, 61% are or have been in academic practice. Regional anesthesia remains an integral part of most respondents' current practice, as evidenced by significant use of regional techniques, active involvement in subspecialty societies, and participation in continuing medical education programs. Academic chairs indicate that fellowship-trained regional anesthesiologists play important roles in resident education and are in demand by academic departments. CONCLUSIONS: This report details how regional anesthesia fellows from 1983 to 2002 were trained and how they currently practice and examines their insights regarding the strengths and weaknesses of past and future regional anesthesia education.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".