Ultrasound-Guided Fine-Needle Aspiration Biopsy of the Thyroid: Methods to Decrease the Rate of Unsatisfactory Biopsies in the Absence of an On-Site Pathologist
Bibliographic record
Abstract
PURPOSE: The rate of unsatisfactory samples from ultrasound-guided fine-needle aspirations of thyroid nodules varies widely in the literature. We aimed to evaluate our thyroid ultrasound-guided fine-needle aspiration biopsy technique in the absence of on-site microscopic examination by a pathologist; determine factors that affect the adequacy rate, such as the number of needle passes and needle size; compare our results with the literature; and establish an optimal technique. MATERIALS AND METHODS: We performed a retrospective review of cytopathology reports from 252 consecutive thyroid ultrasound-guided fine-needle aspiration biopsies performed by a radiologist between 2005 and 2010 in our hospital's radiology department. Sample adequacy, the number of needle passes, and needle size were determined. There was an on-site cytologist who prepared slides immediately after fine-needle aspiration but no on-site microscopic assessment of sample adequacy to guide the number of needle passes that should be performed. Cytopathology biopsy reports were classified as either unsatisfactory or satisfactory samples for diagnosis; the latter consisted of benign, malignant, and undetermined diagnoses. RESULTS: Seventy-seven biopsies were performed with 1 needle pass, 124 with 2 needle passes, and 51 with 3 needle passes. The rates of unsatisfactory biopsies were 33.8%, 23.4% (odds ratio [OR] 0.599 [95% confidence interval {CI}, 0.319-1.123]; P = .110), and 13.7% (OR 0.312 [95% CI, 0.124-0.788]; P = .014), respectively. CONCLUSION: In a hospital in which there is no on-site pathologist, a 3-pass method increases the specimen satisfactory rate by 20% compared with 1 pass, achieves similar rates to the literature, and provides a basis for further improvement of our practice.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".