Bibliographic record
Abstract
PURPOSE OF REVIEW: Thyroid ultrasound (TUS) is a common diagnostic test that can help guide the management of patients with thyroid conditions. Yet, inappropriate use of TUS can lead to harmful unintended consequences. This review aims to describe trends in the use and appropriateness of TUS in practice, drivers and consequences of inappropriate use, and potential solutions to decrease overuse. RECENT FINDINGS: TUS use has increased in the U.S. and is associated with increased diagnosis of thyroid cancer. Between 10-50% of TUSs may be ordered outside of clinical practice recommendations. Patients who receive an inappropriate TUS and are incidentally found to have a thyroid nodule may experience unnecessary worry, diagnostic interventions, and potential overdiagnosis of thyroid cancer. The drivers of inappropriate TUS use are not yet fully understood, but it is likely that a combination of clinician, patient, and healthcare system factors contribute to this problem. SUMMARY: Inappropriate TUS is a factor leading to the overdiagnosis of thyroid nodules and thyroid cancer, resulting in increased healthcare costs and potential harm to patients. To effectively address the overuse of this diagnostic test, it is necessary to gain a deeper understanding of the frequency of inappropriate TUS use in clinical practice and the factors that contribute to it. With this knowledge, interventions can be developed to reduce the inappropriate use of TUS, leading to improved patient outcomes and more efficient use of healthcare resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".