Battle of the axes: Simulation-based assessment of fine needle aspiration biopsies for thyroid nodules
Bibliographic record
Abstract
IMPORTANCE: Ultrasound-guided fine-needle aspiration biopsies (UGFNA) play a crucial role in the diagnosis of thyroid nodules. There are two techniques for performing an UGFNA: short-axis technique and long-axis technique. There is sparsity in the literature regarding the differences between these two techniques. OBJECTIVE: To compare the efficiency between long-axis and short-axis thyroid UGFNA techniques in trainees. Our secondary outcomes were to define the comfort level and learning curves of trainees. DESIGN: A longitudinal prospective cohort study, completed from December 2018 to November 2019, using the Blue Phantom Thyroid Model© for UGFNA. Face and construct validity of the model were verified. Residents completed UGFNA on an assigned nodule using both long-axis and short-axis techniques, the order of which was sequentially allocated. The rate and time to successful biopsy were obtained for both techniques. Biopsy attempts were repeated to establish learning curves. SETTING: Single-center study. PARTICIPANTS: Fourteen Otolaryngology-Head & Neck Surgery residents at the University of Toronto. MAIN OUTCOME MEASURE: Biopsy success and efficiency for novice learners completing UGFNA on a simulated thyroid model using long-axis and short-axis techniques. RESULTS: A trend towards higher odds of successful biopsy using the long-axis technique with no difference in procedure duration was observed (OR = 2.2, p = 0.095, CI = 0.87-5.39). Learning curve graphs appeared heterogenous according to trainee level. Trainees found the long-axis technique easier to perform (10/14, 71%), and the simulator valuable for learning (12/14, 86%). CONCLUSION: Thyroid UGFNA using the long-axis technique may have an increased success rate and is generally favored by trainees for being easier to perform. Thyroid simulators have the potential to increase learner comfort and efficiency with UGFNA.
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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 | 0.003 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".