Composition and Priorities of Multidisciplinary Pediatric Thyroid Programs: A Consensus Statement
Bibliographic record
Abstract
Background: The incidence of pediatric thyroid cancer has been increasing, and care varies due to socioeconomic disparities or differing practice patterns. Clinical guidelines call for care in multidisciplinary teams to minimize variance and provide protocols. Based on expert opinion, we hope to describe the form and function of such multidisciplinary teams for pediatric thyroid programs. Methods: A modified Delphi method to reach consensus statements over two rounds. Twenty-one experts with varying backgrounds responded to each statement on a 9-point Likert scale. Upon completion of the survey, the panel reviewed and shared the results and comments from participants and modified the statements accordingly. This process was repeated such that statements reached consensus, were deemed no consensus, or had no change in the mean. Results: There was an 88% and 83% completion rate for Rounds 1 and 2, respectively. A consensus was observed that there is a distinct definable model of care for pediatric thyroid patients. No consensus was reached for the age range of patients, but programs should care for children with medullary thyroid cancer, differentiated thyroid cancer, and patients with genetic predisposition syndromes. A comprehensive team includes, but is not limited to, a thyroid surgeon, a pediatric endocrinologist, a high-volume fine-needle aspiration (FNA) proceduralist, an oncologist, a nuclear medicine physician, a pediatric pathologist, a pediatric radiologist, and a nurse coordinator. Necessary support services involve care coordination, access to a multidisciplinary tumor board, ability to perform ultrasound-guided FNA, and access to molecular testing. The panel emphasized cross-institutional collaborative research prioritizing guidelines development, disease-specific outcomes, treatment toxicity, and the molecular landscape of thyroid cancer. Conclusions: These consensus statements can be beneficial in improving multidisciplinary care, by describing which elements of pediatric thyroid programs should be consistent across institutions. Overall, the panel agreed that pediatric thyroid centers should provide integrated care with defined team members, services, resources, and research priorities. This model has the potential to standardize various aspects of clinical care and enhance our ability to study patient outcomes, improve health care delivery, and increase scholarly collaboration.
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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 | 0.104 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".