The role of repeat fine needle aspiration in managing indeterminate thyroid nodules
Bibliographic record
Abstract
BACKGROUND: The Bethesda System is the most widely used for reporting fine needle aspiration (FNA) cytology. It recommends a repeat FNA (rFNA) when initial results are category I or III. It is unclear how often rFNA provides additional diagnostic information. We sought to investigate its utility at our institution. METHODS: A retrospective chart review was performed of patients who had a category I or III FNA result and underwent rFNA of the same thyroid nodule between 2013 and 2015 at the QE II Health Sciences Centre in Nova Scotia, Canada. Results of initial FNA and ultrasound characteristics, rFNA, demographic data, surgical details, and pathology were collected. RESULTS: A total of 237 patients (474 thyroid FNAs) were included. Most initial FNAs were category I (82%), the remainder category III (18%). rFNA yielded a different category 60% of the time. However, 60% remained category I or III. rFNA results of benign or malignant were found in 40% of cases; 1% were SFN/SFM. Twenty-seven percent of patients had surgery after rFNA; of those 68% had category I or III rFNA results. Of all nodules that underwent surgery, 46% were malignant, including 32% with category I rFNA results, and 42% category III. CONCLUSIONS: rFNA for category I and III nodules provided a definitive diagnosis in only 40% of cases, which is important for patient counseling. Malignancy rates at our centre were higher for these categories than predicted by Bethesda. Clinical management should consider institution specific malignancy rates, patient factors, and ultrasound findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".