Online TI-RADS Calculator
Bibliographic record
Abstract
Background: Thyroid nodules are common and fine needle aspiration (FNA) or surgery is used to assess for malignancy. Thyroid Imaging, Reporting and Data System (TI-RADS) use ultrasound for non-invasive risk stratification of thyroid nodules and reduce unnecessary biopsies. This project used an online calculator and education to facilitate the application of TI-RADS in clinical practice. Methods: Retrospective review defined the baseline reporting of thyroid nodule ultrasound features. Web-based resource and presentation were used to integrate TI-RADS in reporting thyroid ultrasounds and measure the improvements in comprehensive reporting of thyroid nodules and guiding management. Results: The percentage of thyroid ultrasound reporting using TI-RADS within six months increased from 0% to 27% during the project period. Reports with TI-RADS provided twice as many recommendations compared to reports without TI-RADS. Conclusion: Online TI-RADS calculator and education have successfully facilitated the integration of TI-RADS in thyroid ultrasound reporting to provide more accurate and comprehensive reports and guide management.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".