Future of Thyroid Surgery and Training Surgeons to Meet the Expectations of 2000 and Beyond
Bibliographic record
Abstract
What is the future of thyroid surgery in the new millennium? How can surgeons keep abreast of advances in thyroid endocrinology, genetics, surgical therapy, and other aspects of thyroid disease management? How should surgeons be trained to become highly competent in thyroid disease and to perform safe, effective thyroid operative procedures? Nine internationally recognized endocrine surgeons were asked to express their views on these and related subjects. They noted that advances in molecular biology, pathology, and genetics of thyroid disease should allow more tailored surgical approaches during the twenty-first century. Current training of general surgical residents in thyroid and other types of endocrine surgery is highly variable, which may contribute to increased complication rates and number of second operations. The leadership for addressing these deficiencies and promoting a more organized approach to thyroid disease management should come from national endocrine surgery associations and their leaders. It is incumbent upon endocrine surgeons to maintain their central role in the management of many aspects of thyroid disease. Organizing teams of specialists into thyroid centers (centers of excellence) can (1) increase efficiency; (2) increase quality of care; (3) decrease costs; (4) encourage a more individualized approach to surgery; (5) lower complication rates; and (6) foster innovation in technology and disease management. Two years of additional fellowship training in thyroid and endocrine surgery is now being advocated by increasing numbers of national endocrine surgical associations as the best way to prepare surgeons for society's needs for highly skilled, competent thyroid surgeons of the future.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".