Investigation of thyroid nodules: A practical algorithm and review of guidelines
Bibliographic record
Abstract
BACKGROUND: High resolution ultrasound has led to early detection of subclinical tumors and drastic increase in incidence of thyroid malignancy. To achieve a balance in appropriate investigation without perpetuating an overdiagnosis phenomenon, a concise set of evidence-based recommendations to stratify risk is required. METHODS: We sought to assemble an evidence-based diagnostic algorithm and accompanying pictorial review for workup of thyroid nodules that summarizes the most recent guidelines. In addition, we conducted a literature search and analysis of our imaging databases. RESULTS: Although many imaging features of benign and malignant nodules can be nonspecific, others, such as microcalcifications, lymphadenopathy, and peripheral invasion, are highly suggestive of malignancy. The predictive values of salient imaging characteristics are presented. CONCLUSION: Evidence-based guidelines are available such that a cost-effective algorithm for thyroid nodule workup can be devised. Conservative management with a focus on periodic monitoring is the working clinical consensus on the approach to 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".