Effect of training on resident inter-reader agreement with American College of Radiology Thyroid Imaging Reporting and Data System
Bibliographic record
Abstract
BACKGROUND: The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) was introduced to standardize the ultrasound characterization of thyroid nodules. Studies have shown that ACR-TIRADS reduces unnecessary biopsies and improves consistency of imaging recommendations. Despite its widespread adoption, there are few studies to date assessing the inter-reader agreement amongst radiology trainees with limited ultrasound experience. We hypothesize that in PGY-4 radiology residents with no prior exposure to ACR TI-RADS, a statistically significant improvement in inter-reader reliability can be achieved with a one hour training session. AIM: To evaluate the inter-reader agreement of radiology residents in using ACR TI-RADS before and after training. METHODS: A single center retrospective cohort study evaluating 50 thyroid nodules in 40 patients of varying TI-RADS levels was performed. Reference standard TI-RADS scores were established through a consensus panel of three fellowship-trained staff radiologists with between 1 and 14 years of clinical experience each. Three PGY-4 radiology residents (trainees) were selected as blinded readers for this study. Each trainee had between 4 to 5 mo of designated ultrasound training. No trainee had received specialized TI-RADS training prior to this study. Each of the readers independently reviewed the 50 testing cases and assigned a TI-RADS score to each case before and after TI-RADS training performed 6 wk apart. Fleiss kappa was used to measure the pooled inter-reader agreement. The relative diagnostic performance of readers, pre- and post-training, when compared against the reference standard. RESULTS: = 0.02). No significant differences between the pre- and post-training assessments were found for the variables 'composition', 'echogenicity' and 'margins'. There was a general trend towards improved pooled sensitivity with TI-RADS levels 1 to 4 for the post-training assessment while the pooled specificity was relatively high (76.6%-96.8%) for all TI-RADS level. CONCLUSION: Statistically significant improvement in inter-reader agreement in the assigning TI-RADS level and recommendations after training is observed. Our study supports the use of dedicated ACR TI-RADS training in radiology residents.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".