Ultrasound-guided fine-needle aspiration thyroid biopsies in the otolaryngology clinic.
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy of ultrasound-guided thyroid fine-needle aspiration biopsies (USFNABs) performed in the office setting by an otolaryngologist and to evaluate the specimen adequacy of USFNABs performed in patients whose initial palpation-guided fine-needle aspiration biopsies (PGFNABs) were nondiagnostic. DESIGN: Retrospective chart review. SETTING: Royal Victoria Hospital-McGill University Health Centre, Montreal. METHODS: This is a retrospective analysis of 76 USFNABs performed by an otolaryngologist on consecutive patients over a 6-month period. Each patient had a previous nondiagnostic PGFNAB. Biopsies were performed using a 20-gauge fine needle with a Mylab25 Biosound Esoate ultrasound machine. Samples were then classified according to the adequacy of sample and pathologic findings. MAIN OUTCOME MEASURE: Specimen adequacy rate. RESULTS: Sixty-six patients underwent 76 USFNABs. The sample included 57 females and 9 males (mean age 51.1 and 55.4 years, respectively). The specimen adequacy rate was 90.8% (69 of 76). Among the adequate specimens, 2 (2.6%) were malignant, 6 (7.9%) were suspicious for malignancy, 43 (56.6%) were benign, and 18 (23.7%) were follicular or Hürthle cell lesions (indeterminate). CONCLUSION: Our experience demonstrates that USFNAB performed in the clinic by an otolaryngologist is a promising tool for improving specimen adequacy for nodules initially classified as nondiagnostic. USFNAB also avoids the need for radiologic consultation, thus improving efficacy in the workup of 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".