The prevalence and prognostic significance of estrogen receptor beta expression in non-small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Estrogen receptor beta (ERβ) is the predominant estrogen receptor (ER) expressed in non-small cell lung cancer (NSCLC); however, due to methodological disparities among prior studies, the prognostic value of ERβ expression in NSCLC remains unclear. Our objective was to apply improved detection and analysis techniques to assess the prognostic value of ERβ expression in NSCLC. METHODS: A tissue microarray (TMA) was used which contained resected and biopsy specimens from 299 patients diagnosed at a single center with stages I-IV NSCLC. Sections of this array were stained using high-sensitivity fluorescence immunohistochemistry, with the well-validated PPG5/10 monoclonal antibody. Digital images of the stained array slides were analyzed using software-based image analysis, which reported ERβ expression as a continuous variable in different subcellular domains. RESULTS: There were no differences in ERβ expression between male and female patients. High expression of ERβ was not a prognostic factor, but was significantly associated with stage IV disease in both tumor and stroma (P<0.001). In multivariable analysis, a high nuclear/cytoplasmic (N/C) ratio of ERβ expression was significantly associated with shorter overall survival, based on expression in the tumor [hazard ratio (HR): 1.65; 95% confidence interval (CI): 1.25-2.19; P<0.001] and in the stroma (HR: 1.57; 95% CI: 1.16-2.12; P=0.003). CONCLUSIONS: These results suggest that subcellular localization of ERβ, but not absolute expression, is a prognostic factor in NSCLC.
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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 | 0.002 |
| 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 | 0.000 | 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".