Blood arsenic levels and the risk of familial breast cancer in Poland
Bibliographic record
Abstract
Arsenic is recognized as a potent carcinogen at high concentrations, but the relationship between environmental arsenic and breast cancer risk has not well been studied. Most research has focused on the effect of arsenic in populations with high endemic exposure, and not in populations with arsenic levels within normal limits. We sought to determine if blood arsenic levels predict the risk of breast and other cancers risk among women in northern Poland. The cohort consisted of 1,702 healthy women, aged 40 and above, identified between 2010 and 2017. Blood arsenic level was determined by inductively coupled plasma mass spectrometry. After an average of 4.5 years of follow-up (range 0.7-7.3 years), there were 110 incident cases of cancer diagnosed in the cohort, including 68 cases of breast cancer. Women in the highest quartile of arsenic had a highly significant 13-fold increased risk of developing breast cancer, compared to women in the lowest quartile (hazard ratio [HR] = 13.2; 95% confidence interval [CI] 4.02-43.0). Results were similar for arsenic and all incident cancers (HR quartile 4 vs. quartile 1 = 13.3; 95% CI 4.78-37.0). If confirmed, our study suggests that the blood arsenic level may be a useful predictive marker of cancer risk in women.
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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 | 0.001 |
| 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".