ODP578 Utility of the ACR-TIRADS Score – A Survey of Primary Care Providers
Bibliographic record
Abstract
Abstract Background The American College of Radiology – Thyroid Imaging Reporting and Data System (ACR-TIRADS) score is an ultrasound-based tool used to assess the risk of malignancy in thyroid nodules. Despite evidence supporting its efficacy, the use of this score in clinical practice has not yet been reported. Since primary care providers commonly identify and manage thyroid nodules, we aimed to determine 1) current diagnostic approach to thyroid nodules and 2) perceived utility of ACR-TIRADS in Canadian primary care providers. Methods We conducted a cross-sectional survey study with family medicine physicians and nurse practitioners working in Canada. The 23-question survey was developed and distributed electronically on Qualtrics using convenience sampling from August 31 2021 to January 3, 2022. Sociodemographic and other quantitative questions using Likert scales were analyzed using descriptive statistics; Likert scale responses were analyzed as proportion of individuals who agreed or strongly agreed. Qualitative data from free text answers was assessed using content analysis. Results One hundred and four primary care providers responded to our survey. Of these, 90 (87%) completed the entire survey and were included. Fifty-six percent were female, 58% were aged <40 years, and 48% were in practice for five or fewer years. In terms of management of thyroid nodules, only 47% were confident in their ability to risk-stratify. Ninety-four percent of participants risk-stratified using imaging characteristics, but only 57% of respondents reported using a risk stratification tool. Despite 64% percent of participants agreeing with being familiar with the ACR-TIRADS score, only 28% used this scoring system, and 80% of participants had a desire to learn more about it. Only 68% of physicians believed the score was present on ultrasound reports, and <5% requested it on reports themselves. After being shown a diagram demonstrating the ACR-TIRADS scoring system, 93% thought that the ACR-TIRADS score was useful. Of the 31 (34%) participants who were not initially familiar with the score, 61% would use the ACR-TIRADS score more, and 77% would ask radiology to report the ACR-TIRADS score more often. In terms of qualitative data, respondents believed educational tools and increased reporting by radiologists would increase use of ACR-TIRADS. Conclusion In this Canadian survey study, the majority of respondents were not confident with risk stratification of thyroid nodules, and only half used a risk stratification tool. With education, almost all participants thought ACR-TIRADS was useful, and most participants would use it more often. Further interventions to educate primary care providers regarding the ACR-TIRADS score may help enhance its uptake and improve systematic management of thyroid nodules. Presentation: No date and time listed
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".