Surgeon-performed ultrasound guided fine-needle aspirate biopsy with report of learning curve; a consecutive case-series study
Bibliographic record
Abstract
BACKGROUND: Fine-needle aspiration biopsy has become the standard of care for the evaluation of thyroid nodules. More recently, the use of ultrasound guided fine-needle aspiration biopsy (UG-FNAB) has improved adequacy of sampling. Now there has been improved access to UG-FNAB as ultrasound technology has become more accessible. Here we review the adequacy rate and learning curve of a single surgeon starting at the adoption of UG-FNAB into surgical practice. METHODS: UG-FNABs performed at Sunnybrook Health Sciences Centre from 2010 to 2015 were reviewed retrospectively. Nodule characteristics were recorded along with cytopathology and final pathology reports. Chi-square analysis, followed by the reporting of odds ratios with confidence intervals, were used to assess the statistical significance and frequencies, respectively, of nodule characteristics amongst both diagnostic and non-diagnostic samples. A multiple regression analysis was conducted to determine if any nodule characteristic were predictive of adequacy of UG-FNABs. The learning curve was assessed by calculating the eventual adequacy rates across each year, and its statistical significance was measured using Fischer's Exact Test. RESULTS: In total 423 biopsies were reviewed in 289 patients. The average nodule size was 23.05 mm. When examining if each patient eventually received a diagnostic UG-FNAB, regardless of the number attempts, adequacy was seen to increase from 70.8% in 2010 to, 81.0% in 2011, 90.3% in 2012, 85.7% in 2013, 89.7% in 2014, and 94.3% in 2015 (Fischer's Exact Test, p = 0.049). Cystic (χ(2) = 19.70, p <0.001) nodules were found to yield higher rates of non-diagnostic samples, and their absence are predictive of obtaining an adequate biopsy as seen in a multiple regression analysis (p < 0.001) Adequacy of repeat biopsies following an initial non-diagnostic sample was 75.0%. CONCLUSIONS: Surgeons are capable of performing UG-FNAB with a learning curve noted to achieve standard adequacy rates. Cystic nodules are shown to yield more non-diagnostic samples in the surgeon's office.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".