Thyroid Fine-needle aspiration biopsy: An evaluation of its utility in a community setting
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer rates are on the rise worldwide with over 5000 new cases estimated in Canada in 2012. The American Thyroid Association recommends the use of fine-needle aspiration biopsy (FNA) in the workup of thyroid nodules. Studies show that thyroid FNA accuracy may vary based on interpretation by cytopathologists in academic versus community centres. To date, there has been no literature published addressing the accuracy or utility of preoperative FNA in a Canadian community center. Our goals were to demonstrate the accuracy of thyroid FNA at our centre, and to compare our results to those published in the literature. METHODS: Medical records for patients who underwent thyroidectomy performed by two otolaryngologists in Fredericton, NB, between September 2008 and February 2013 were reviewed. 125 patients with 197 FNAs were analyzed. Fisher's Exact test was used to compare the malignancy rates in each FNA category, and Chi-Square test was used for FNA distribution comparison. RESULTS: The distribution of all FNA diagnoses at our centre was as follows: 38 (19%) benign, 100 (51%) inconclusive, 8 (4%) suspicious for malignancy, 2 (1%) malignant, and 49 (25%) unsatisfactory. FNA distribution was significantly different between our centre and comparison centres (Chi-Square p < 0.05). Our malignancy rates within each category using each FNA sample as a data point were 26.3%, 29.0%, 75%, 100% and 12.2% respectively. Comparison to other community studies revealed that we have significantly higher malignancy rates with benign FNAs (Fisher's exact p = <0.05). Analysis using our most malignant FNA data yielded similar results. CONCLUSION: Thyroid FNA accuracy varies between institutions, and this may affect its utility in the workup of a thyroid nodule at some centres. Expert cytopathology opinions may be an asset in interpreting FNA samples in small community centres where volumes are relatively low, however our data do not support this assertion. It is essential that physicians continue to use clinical judgment first and foremost when evaluating thyroid nodules.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".