Interspecialty and intraspecialty differences in the management of thyroid nodular disease and cancer
Bibliographic record
Abstract
INTRODUCTION: The management of thyroid cancer includes multiple medical specialties. Physicians from different specialties may vary in opinion regarding the optimal investigation and treatment of patients. Little data exist evaluating the differences within or between various specialties treating thyroid disease. This study aims to examine responses from a variety of specialty physicians closely involved in the medical or surgical management of thyroid disease to provide evidence as to whether any difference exists. METHODS: A cross-sectional survey of attendees at the 5(th) Biennial Course on the Management of Thyroid Nodular Disease and Cancer was conducted using an anonymous electronic touch pad system. Touch pads were given to 213 attendees who were asked to respond to 44 questions. This study analyzes the responses obtained from 19 selected questions (43%) and compares the results between endocrinologists (n = 48), general surgeons (n = 41), otolaryngologists (n = 61), and pathologists (n = 20). RESULTS: Responses were obtained from 69% of endocrinologists, 68% of general surgeons, 72% of otolaryngologists, and 65% of pathologists. Statistically significant interspecialty differences were observed in 12 (63%) of 19 questions. Each question and a summary of responses from all touch pads were recorded. CONCLUSIONS: Significant differences in the attitudes toward, and presumably the practice of, managing thyroid nodular disease and cancer exist between specialties. An understanding of these differences is helpful when working as a multidisciplinary team to optimize patient care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".