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.
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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 | 0.093 | 0.205 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.054 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".