A systematic review and meta-analysis of palpation versus ultrasound-guided fine needle aspiration of thyroid nodules
Bibliographic record
Abstract
Background: Thyroid nodules are a common clinical finding. Fine-needle aspiration (FNA) is the most widely accepted diagnostic tool used to differentiate malignant and benign thyroid nodules. FNA can be carried out by manual palpation of the nodule or with ultrasound guidance. Existing clinical practice guidelines give mixed recommendations regarding the use of ultrasound guidance for thyroid FNA. Given the inconsistencies in the guidelines, we performed a systematic review and meta-analysis to compare the diagnostic accuracy of palpationguided fine needle aspiration (PG-FNA) versus ultrasound-guided fine needle aspiration (USG-FNA). Methods: Studies comparing PG-FNA and USG-FNA were identified through a search of PubMed, the Cochrane Library, and Embase (1990- December 2011). Titles and abstracts were reviewed and studies were selected for a full text review. Meta-analysis of included studies was performed to estimate the average sensitivity, specificity, and rate of inadequate samples for each technique. Results: We screened 1934 citations and selected seven studies meeting our predefined inclusion criteria. The pooled sensitivity of USG-FNA was found to be higher than PG-FNA [0.91 (CI=0.82, 1.0) and 0.79 (CI=0.69, 0.85), respectively]. The pooled specificity of USG-FNA was also found to be slightly higher than PG-FNA [0.77 (CI=0.69, 0.85) and 0.73 (CI=0.64, 0.81), respectively]. The mean rate of inadequate samples was higher for PG-FNA at 14.7% versus 8.4% for US-FNA. Conclusions: Our findings show that USG-FNA has a higher diagnostic accuracy than PG-FNA and a lower rate of inadequate samples. Overall, these findings suggest an advantage to the use of USG-FNA over PG-FNA.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.044 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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 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".