Galectin‐3 as Adjunct to HBME‐1 Staining of Fine‐Needle Aspiration Biopsy Samples of Thyroid Nodules
Bibliographic record
Abstract
Objectives: (1) Examine the added value of galectin‐3 to HBME‐1 staining on fine‐needle aspiration (FNA) of thyroid nodules. (2) Correlate galectin‐3 on FNA with pathology results both on FNA and final surgical pathology. Methods: A retrospective review of the charts of 53 patients undergoing FNA at the Jewish General Hospital in Montreal, Canada, in 2013 for the investigation of thyroid nodules whose FNA samples underwent HBME‐1 and galectin‐3 staining in addition to pathological examination. Results: Of 53 FNABs, 20 (37%) were galectin‐3 positive, 24 (45%) were galectin‐3 negative, and 9 (17%) were equivocal. With regard to HBME‐1, 15 (28%) samples were positive, 32 (60%) were negative, and 6 (11%) were equivocal. Both stains correlated strongly with each other (P <. 001) as well as with pathologist examinations of the FNA samples (P <. 001 for both galectin‐3 and HBME‐1). Eleven patients underwent total or partial thyroidectomy. In correlation with surgical pathology, galectin‐3 was correctly positive in 5 patients, correctly negative once, falsely positive once, falsely negative once, and indeterminate in 3 patients. HBME‐1 was correctly positive in 7 patients, correctly negative in 2 patients, falsely negative once, indeterminate once, and without false positives. When combining HBME‐1 and galectin‐3, 8 true positives, 1 true negative, and 1 false positive were observed. No false negatives were found when combining both stains. Conclusions: Galectin‐3 may be a useful adjunct to HBME‐1 staining in FNA of thyroid nodules in order to detect papillary thyroid carcinoma. Further research is needed to confirm these results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".