Application of immunohistochemistry to thyroid neoplasms.
Bibliographic record
Abstract
CONTEXT: Thyroid lesions with nodular architecture and follicular pattern of growth often pose difficulties in accurate diagnosis during the assessment of cytologic and histologic specimens. The diagnosis of follicular neoplasm on cytology or of follicular tumor of uncertain malignant potential on histology is likely to cause confusion among clinicians and delay effective management of these lesions. Occasionally, thyroid tumors represent unusual or metastatic lesions and their accurate diagnosis requires immunohistochemical confirmation. OBJECTIVE: To review the literature on the applications of immunohistochemistry in the differential diagnosis of thyroid tumors. DATA SOURCES: Relevant articles indexed in PubMed (National Library of Medicine) between 1976 and 2006. CONCLUSIONS: Our review supports the use of ancillary techniques involving a panel of antibodies suitable for immunohistochemistry and molecular analysis in the assessment of thyroid nodules. These tools can improve diagnostic accuracy when combined with standard morphologic criteria.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".