Ultrasound Imaging in Active Surveillance of Small, Low-Risk Papillary Thyroid Cancer
Bibliographic record
Abstract
The recent surge in the incidence of small papillary thyroid cancers (PTCs) has been linked to the widespread use of ultrasonography, thereby prompting concerns regarding overdiagnosis. Active surveillance (AS) has emerged as a less invasive alternative management strategy for low-risk PTCs, especially for PTCs measuring ≤1 cm in maximal diameter. Recent studies report low disease progression rates of low-risk PTCs ≤1 cm under AS. Ongoing research is currently exploring the feasibility of AS for larger PTCs (<20 mm). AS protocols include meticulous ultrasound assessment, emphasis on standardized techniques, and a multidisciplinary approach; they involve monitoring the nodules for size, growth, potential extrathyroidal extension, proximity to the trachea and recurrent laryngeal nerve, and potential cervical nodal metastases. The criteria for progression, often defined as an increase in the maximum diameter of the PTC, warrant a review of precision and ongoing examinations. Challenges exist regarding the reliability of volume measurements for defining PTC disease progression. Although ultrasonography plays a pivotal role, challenges in assessing progression and minor extrathyroidal extension underscore the importance of a multidisciplinary approach in disease management. This comprehensive overview highlights the evolving landscape of AS for PTCs, emphasizing the need for standardized protocols, meticulous assessments, and ongoing research to inform decision-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".