American Head and Neck Society Endocrine Section clinical consensus statement: North American quality statements and evidence‐based multidisciplinary workflow algorithms for the evaluation and management of thyroid nodules
Bibliographic record
Abstract
BACKGROUND: Care for patients with thyroid nodules is complex and multidisciplinary, and research demonstrates variation in care. The objective was to develop clinical guidelines and quality metrics to reduce unwarranted variation and improve quality. METHODS: Multidisciplinary expert consensus and modified Delphi approach. Source documents were workflow algorithms from Kaiser Permanente Northern California and Cancer Care of Ontario based on the 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer. RESULTS: A consensus-based, unified preoperative, perioperative, and postoperative workflow was developed for North American use. Twenty-one panelists achieved consensus on 16 statements about workflow-embedded process and outcomes metrics addressing safety, access, appropriateness, efficiency, effectiveness, and patient centeredness of care. CONCLUSION: A panel of Canadian and United States experts achieved consensus on workflows and quality metric statements to help reduce unwarranted variation in care, improving overall quality of care for patients diagnosed with thyroid nodules.
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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 | 0.056 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".