Kindred spirits of the endocrines: The training of the future endocrine surgeons
Bibliographic record
Abstract
The growth of knowledge and complexity now seen in General Surgery, has led to the sub-specialization of the discipline. Although it is considered by some to have led to the fragmentation of General Surgery and the erosion of the specialty as we know it today, others would argue that it has and will continue to lead to a stronger division and a higher standard of care. Most would argue that a higher standard of care in focus areas stimulates research and research, in turn, improves the quality of education and training. Ultimately, improved education and training leads to better patient care. Organ-specific specialization such as orthopedics and urology evolved from General Surgery and demonstrates this principle. Further sub-specialization is likely inevitable, if the discipline of General Surgery is to remain a desired specialty. Endocrine surgery has evolved into a sub-specialty of General Surgery, and over the last few decades has matured as a discipline. With this maturation comes the responsibility of defining the standard of care to be provided by surgeons involved in endocrine surgery. To achieve this goal, endocrine surgical associations and societies must set the standard of training both at the residency and postgraduate level. Where we are as a sub-specialty, where we came from, and what it will take to meet this goal are discussed.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".