Stromal tissue as an adjunct tool in the diagnosis of follicular thyroid lesions by fine-needle aspiration biopsy
Bibliographic record
Abstract
BACKGROUND: The stroma in fine-needle aspiration biopsy (FNAB) of thyroid lesions has not been well investigated. DESIGN: We studied 256 consecutive cases of thyroid FNAB prepared with traditional smear technique. The stroma was categorized: Type 1a consisted of long (more than 3 mm), broad bands composed of mesh containing collagen fibrils thickened by entrapped blood components and follicular cells. Type 1b consisted of dense strands/bands. Type 2 was similar to Type 1a but with shorter (<2 mm) and looser stromal strands. RESULTS: Types 1a and b showed straight/curved/circular branching patterns suggestive of incomplete frameworks of nodular/papillary architectures or fragments of capsule. Type 1b stroma likely represented thick/collagenized fibrous septae. Incomplete or complete rings of small encapsulated tumor were occasionally identified. These frameworks of stroma were frequently associated with multinodular goiters (MNGs) which are often hypocellular and follicular neoplasms/papillary thyroid carcinoma with increased cellularity. Type 2 was associated with microfollicles in encapsulated neoplasms or with macrofollicles in MNG. Follicular lesions of unknown significance (n = 41) either negative (n = 26) or positive (n = 15) for carcinoma in subsequent follow-up were frequently associated with stroma characteristic of MNG and carcinoma, respectively. CONCLUSION: The preservation of the in vivo architecture of Type 1 is likely due to its elasticity. Recognition of the stromal architecture will likely facilitate the diagnosis.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".