Ultrasound‐Guided Fine‐Needle Aspiration of Thyroid Nodules: Does Size Matter?
Bibliographic record
Abstract
Objectives: Evaluate the accuracy and predictive values of ultrasound guided fine‐needle aspiration (USFNA) of nodules ≥4cm compared to smaller nodules. Authors have reported that fine‐needle aspiration (FNA) biopsies of thyroid nodules ≥4cm are unnecessary since they often yield inaccurate results compared to nodules <4cm. They therefore recommend diagnostic thyroid lobectomies for nodules ≥4cm and FNA for smaller nodules. Methods: A retrospective study at the McGill University Thyroid Cancer Center was performed on patients between 2006‐2012 comparing the USFNA and post‐operative pathology diagnoses of nodules ≥4cm versus those <4cm. Pre‐operative USFNA results were divided into benign, indeterminate and malignant/suspicious for malignancy subgroups. Postoperative results were separated into benign and malignant groups. SPSS was used for data analysis using the chi‐square method. Results: There were 225 patients with nodules ≥4cm and 773 patients with nodules <4cm. The sensitivity, specificity, positive predictive value, and negative predictive value for USFNA of nodules ≥4cm were 84.62% (confidence interval [CI] 71.91‐93.10), 91.49% (CI 79.6‐97.58), 91.67% (CI 80.0‐97.63) and 84.31% (CI 71.4–92.95), respectively. The sensitivity, specificity, positive predictive value and negative predictive value for USFNA of nodules <4cm were 90.48% (CI 86.1‐93.8), 85.92% (CI 75.6‐93.02), 95.8% (CI 92.41‐97.96) and 71.76% (CI 60.95‐81.0), respectively. The difference in diagnostic accuracy of USFNA between both groups was not statistically significant (P > 0.05). Conclusions: This study shows that USFNA of nodules ≥4cm is as accurate as smaller nodules. It is therefore suggested that these nodules be managed similarly to their smaller counterparts.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".