Prognostic and Predictive Value of Low Estrogen Receptor Expression in Breast Cancer
Bibliographic record
Abstract
Purpose: Anti-hormonal therapy (tamoxifen) is recommended for estrogen receptor (er)–positive breast cancer (bca); however, its effect on low-receptor cancers is unclear. We retrospectively evaluated the effect of adjuvant tamoxifen in patients with weakly er-positive bca. Methods: We identified 2221 bca patients who had been er-tested by ligand-based assay (lba) during 1976–1995 and who had been treated and followed until 2008. Cox proportional hazards models adjusted for age, body mass index, tumour size, nodal status, surgery, and chemotherapy were used to assess the effect of er level on bca survival in patients who received tamoxifen. Results: Overall, 17% (383) of patients were within 0–3 fmol/mg cytosol protein, and 12% (266) were within 4–9 fmol/mg cytosol protein. Patients with er levels of 0–3, 4–9, 10–19, 20–49, and 50 fmol/mg or more cytosol protein had 20-year bca survival rates of 56%, 56%, 63%, 71%, and 60% respectively. Of the 2221 patients studied, 661 (29.8%) received anti-hormonal therapy. Within the latter group, er levels of 0–3, 4–9, 10–19, 20–49, and 50 fmol/mg or more cytosol protein were associated with a hazard ratio for lower bca mortality: respectively, 1.00 (reference), 0.59 (p = 0.09), 0.19 (p < 0.0001), 0.26 (p < 0.0001), and 0.31 (p < 0.0001)—the risk reduction being significant only for er levels of 10 fmol/mg or more cytosol protein. Conclusions: Tamoxifen use in bca patients with a weakly positive er status (4–9 fmol/mg cytosol protein), compared with those having higher er levels (≥10 fmol/mg cytosol protein), is not associated with a significantly lower bca-specific mortality. Our results do not support treatment with anti-hormonal therapy for bca patients with a weakly positive er status as identified by lba.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".