Fine needle cytology of complex thyroid nodules
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether a preliminary aspiration (ASP) of the cystic component and/or using spinal needles in complex thyroid nodules (CTN) could improve the adequacy of cytological sampling. METHODS: Between January 2004 and December 2006, 386 consecutive patients with CTN were enrolled in this prospective investigation. Ultrasound (US) fine needle aspiration cytology (FNAC) of the solid component of the nodule (one nodule per patient) was performed using two different 25 gauge needles, with (Yale Spinal, YS) or without (Neolus, NS) a stylet, in alternate sequence on consecutive patients. In addition, a subgroup of patients presenting larger cystic component (approximately 50%) was submitted to total aspiration of the cystic component (ASP+) or not submitted (ASP-) before US-FNAC, in alternate sequence within each needle type group. All the samplings were performed by a single endocrinologist. RESULTS: Adequate specimens were observed in 163 (84.5%) and 183 (94.8%) nodules investigated by NS and YS respectively. Sampling with the stylet needle was associated with an overall significant reduction of non-diagnostic specimens (15.5% vs 5.2% by NS and YS respectively, P < 0.001). The favourable result obtained with YS was independent from preliminary aspiration of the cystic component (ASP+: 14.8% vs 5.7% by NS and YS; ASP-: 16.2% vs 4.8%, not significant). A logistic regression analysis, taking into account nodule size and presence of intranodal vascularity at eco-colour evaluation of the solid component, confirmed that needle type was the only significant predictor of successful sampling (odds ratio 3.6 (95% confidence interval 1.7-7.6), P < 0.001). CONCLUSIONS: Our data show that adopting stylet needles to perform FNAC in CTN may significantly improve the percentage of adequate sampling. On the other hand, preliminary aspiration of CTN with large cystic component does not add any advantage.
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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 | 0.004 |
| 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".