Improvement in neck ultrasound report quality following the implementation of European Thyroid Association guidelines for postoperative cervical ultrasound for thyroid cancer follow-up, a prospective population study
Bibliographic record
Abstract
Objective: The aim of this study was to prospectively evaluate the quality of postoperative neck ultrasound (POU) for thyroid cancer patients after implementing European Thyroid Association (ETA) guideline-based POU assessment. Methods: Our analysis involved 672 differentiated thyroid cancer patients. POU report quality was compared between the implementation radiology group (IRG), which implemented ETA guideline-based assessment in 2018, and all non-implementation radiology groups (NIRG). Differences in POU quality were evaluated before and after the implementation of guideline-based assessment. Additionally, we evaluated the ability of serum thyroglobulin (Tg) level <0.2 ng/mL or between 0.21 and 0.99 ng/mL and normal POU lesion status at 1-year follow-up to predict the absence of persistent disease or relapse at 3-year follow-up. Results: IRG had significantly higher mean utility scores for POU reports of abnormal thyroid bed nodules compared to NIRG (P < 0.001). IRG's POU reports for suspicious nodules and lymph nodes were considered sufficient in 94% and 85% of cases, respectively, compared to 45% and 68% for NIRG. For patients with normal US lesion status and Tg <0.2 ng/mL or Tg 0.21-0.99 ng/mL at 1-year follow-up, the negative predictive values were 96% for both. Conclusions: Implementation of 2013 ETA POU-reporting guidelines allowed for the provision of high-quality POU reports, which may lead to increased accuracy in assessing the response to treatment and in estimating the risk of recurrence of thyroid cancer and likely reduce unnecessary repeat POU or FNA.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".