Abstract A81: Relationships between expression of TGF-β factors and breast tumor characteristics in a population-based study from Poland
Bibliographic record
Abstract
Transforming growth factor-beta (TGF-β signaling has been implicated in breast development and carcinogenesis. However, the associations between expression of individual members of the TGF-β superfamily and breast cancer characteristics remains undefined. To address this gap, we evaluated the relationship of expression of TGF-β signaling factors to clinically important tumor characteristics among 842 invasive breast carcinomas identified in a population-based case-control study conducted in Poland. We evaluated immunohistochemical stains performed on tissue microarrays for six components of the TGF-β signaling pathway, including four TGF-β ligands [intracellular TGF-β1, extracellular TGF-β1, TGF-β2, and TGF-β3], the TGF-β type II receptor (TGF-βR2), and the downstream transcriptional modulator, phosphorylated-SMAD2 (p-SMAD2). Three pathologists independently read two of the six stains and scored the staining as negative, equivocal, weakly positive, or strongly positive, depending on the stain. Re-examination of 40 randomly selected spots demonstrated satisfactory intra-observer agreement for all stains (weighted kappa ≥75%). Most tumors were positive for extracellular TGF-β1 (77%), TGF-β2 (89%), TGF-β3 (92%), and TGF-βR2 (71%), whereas expression of intracellular TGF-β1 was detected in 32% and p-SMAD2 in 59% of cases. Lobular histology was associated with expression of extracellular TGF-β1 (χ2 p=
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".