Aspirin Increases the Risk of Nondiagnostic Yield of Fine-Needle Aspiration and Biopsy of Thyroid Nodules
Bibliographic record
Abstract
Abstract Background: The link between the diagnostic yield of thyroid fine-needle aspiration and biopsy (FNAB) in patients taking antithrombotic or anticoagulant medications (AT/AC) remains poorly characterized. Objectives: We studied the risk of obtaining a nondiagnostic sample with ultrasound-guided thyroid FNAB in patients taking AT/AC medications. Methods: This is a retrospective cohort study using medical records of 556 patients who underwent thyroid FNAB. All cytology samples were reported using the Bethesda classification. For patients with a nondiagnostic cytology, logistic regression was used to calculate OR for patients taking AT/AC medications. Multivariate regression was used to adjust for potential confounding variables including age, cystic ultrasound features, presence of eggshell calcifications, number of passes performed, cystic aspirate on FNAB, and position of the nodule. Results: Out of 556 patients, cytology results were available for 547 patients. Of these, 46 subjects were taking aspirin and 1 was on warfarin. Among the entire cohort, 17.5% of the subjects had a nondiagnostic cytology. Among the patients on AT/AC medications, 34% had a nondiagnostic result compared to 16% for those not taking them (OR = 2.70, p = 0.003). The subgroup of patients taking aspirin had similarly higher odds of a nondiagnostic cytology (OR = 2.78, p = 0.002). These differences remained statistically significant after multivariate adjustment. Conclusions: This is the first study to demonstrate a 3-fold independently greater risk of a nondiagnostic FNAB cytology in patients taking aspirin. Our results highlight the importance of evaluating the need for continuation of aspirin in patients undergoing thyroid FNAB as this may impact the diagnostic yield of the procedure.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".