The Effect of Blood Selenium Level on the pCR Rate in Breast Cancer Patient Receiving Neoadjuvant Chemotherapy
Bibliographic record
Abstract
Purpose: Among patients treated with neoadjuvant chemotherapy (NAC), a high survival rate is observed for those who experience a pathological complete response (pCR). Various tumor factors are predictive of a pCR, but few host factors have been studied.We sought to inquire whether or not a patient’s blood selenium level prior to treatment was predictive of a pCR. Methods: We studied 329 women diagnosed with primary invasive breast cancer who were treated with neoadjuvant chemotherapy (NAC). We included patients with HER2-positive (n = 183) or triple-negative breast cancer (n = 146). Blood was collected before the initiation of treatment. Blood levels of selenium were quantified by mass spectroscopy. Each patient was assigned to one of three tertiles based on the distribution of blood selenium levels in the entire cohort. Patients with triple-negative breast cancer (TNBC) were treated with a range of combination chemotherapies. Patients with HER2-positive breast cancer received anti-HER2 treatment based on trastuzumab alone or trastuzumab and pertuzamab. After treatment, each patient was classified as having pCR or no pCR. Results: In the entire cohort, the pCR rate was 59.0% for women in the highest tertileof blood selenium (≥107.19 μg/L) compared to 39.0% for women in the lowest tertile (≤94.29 μg/L) (p = 0.003). Conclusions: A high selenium level is predictive of pCR in women treated for HER2-positive or triple-negative breast cancer. If confirmed, this observation may lead to a study investigating if selenium supplementation improves pCR rates and survival in breast cancer women receiving NAC.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".