Fine‐needle aspiration biopsies in the management of indeterminate follicular and Hurthle cell thyroid lesions
Bibliographic record
Abstract
OBJECTIVES: To determine the value of fine-needle aspiration biopsies (FNABs) of the thyroid and stratify the risk of malignancy within the indeterminate FNAB diagnostic category at our institution. STUDY DESIGN: Case series with chart review of preoperative FNABs of consecutive patients who underwent total thyroidectomy between 2005 and 2007. SUBJECTS AND METHODS: A total of 115 cases were reviewed, and FNABs were categorized into four groups: benign, positive or suspicious for malignancy, indeterminate (follicular or Hurthle cell lesions), and nondiagnostic. Cytohistologic correlation was then established. RESULTS: The accuracy of FNAB in detecting thyroid malignancy was 88 percent with false-negative and false-positive rates of 13 percent and 7 percent, respectively. Overall, 52 percent of the indeterminate cases were carcinomas (48 percent of follicular lesions and 62 percent of Hurthle cell lesions). In the presence of cytologic atypia, the rate of malignancy increased to 75 percent and 83 percent for the follicular and Hurthle cell lesions, respectively. CONCLUSIONS: FNAB is an accurate and helpful method for the evaluation of thyroid nodules with results directly correlating with management. Surgery should be considered for FNABs categorized as indeterminate, especially in the presence of cytologic atypia. Because of the high false-negative rate, benign FNABs require close follow-up with ultrasound examination and periodic biopsies.
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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 | 0.000 | 0.000 |
| 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".