Inter‐Rater Reliability of Thyroid Ultrasound Risk Criteria: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: The most commonly employed diagnostic criteria for identifying thyroid nodules include Thyroid Imaging and Reporting Data System (TI-RADS) and American Thyroid Association (ATA) guidelines. The purpose of this systematic review and meta-analysis is to determine the inter-rater reliability of thyroid ultrasound criteria. METHODS: We performed a library search of MEDLINE (Ovid), EMBASE (Ovid), and Web of Science for full-text articles published from January 2005 to June 2022. We included full-text primary research articles that used TI-RADS and/or ATA guidelines to evaluate thyroid nodules in adults. These included studies must have calculated inter-rater reliability using any validated metric. The Quality Appraisal for Reliability Studies (QAREL) was used to assess study quality. We planned for a random-effects meta-analysis, in addition to covariate and publication bias analyses. This study was performed in accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines and registered prior to conduction (International prospective register of systematic reviews-PROSPERO: CRD42021275072). RESULTS: Of the 951 articles identified via the database search, 35 met eligibility criteria. All studies were observational. The most commonly utilized criteria were ACR Thyroid Imaging and Reporting Data System (TI-RADS) and/or ATA criteria, while the majority of studies employed Κ statistics. For ACR TI-RADS, the pooled Κ was 0.51 (95% confidence interval [CI]: 0.42, 0.57; n = 7) while for ATA, the pooled Κ was 0.52 (95% CI: 0.37, 0.67; n = 3). Due to the small number of studies, covariate or publication bias analyses were not performed. CONCLUSION: Ultrasound criteria demonstrate moderate inter-rater reliability, but these findings are impacted by poor study quality and a lack of standardization. Laryngoscope, 133:485-493, 2023.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".