American Thyroid Association Statement on the Essential Elements of Interdisciplinary Communication of Perioperative Information for Patients Undergoing Thyroid Cancer Surgery
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer specialists require specific perioperative information to develop a management plan for patients with thyroid cancer, but there is not yet a model for effective interdisciplinary data communication. The American Thyroid Association Surgical Affairs Committee was asked to define a suggested essential perioperative dataset representing the critical information that should be readily available to participating members of the treatment team. METHODS: To identify and agree upon a multidisciplinary set of critical perioperative findings requiring communication, we examined diverse best-practice documents relating to thyroidectomy and extracted common features felt to enhance precise, direct communication with nonsurgical caregivers. RESULTS: Suggested essential datasets for the preoperative, intraoperative, and immediate postoperative findings and management of patients undergoing surgery for thyroid cancer were identified and are presented. For operative reporting, the essential features of both a dictated narrative format and a synoptic computer format are modeled in detail. The importance of interdisciplinary communication is discussed with regard to the extent of required resection, the final pathology findings, surgical complications, and other factors that may influence risk stratification, adjuvant treatment, and surveillance. CONCLUSIONS: Accurate communication of the important findings and sequelae of thyroidectomy for cancer is critical to individualized risk stratification as well as to the clinical issues of thyroid cancer care that are often jointly managed in the postoperative setting. True interdisciplinary care is essential to providing optimal care and surveillance.
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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 | 0.052 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".