Bibliographic record
Abstract
To the Editor: The article by Nichols et al1 has addressed an important and complex issue. Because increasing birth order and younger maternal age are likely to be associated with lower levels of exposure to potentially carcinogenic environmental pollutants in breastmilk, the authors postulated that adult breast cancer risk would be lower in breastfed women with higher birth order, and in those breastfed by younger mothers. Their results showed that higher birth order is associated with reduced risk of breast cancer among those who were breastfed in infancy, but younger maternal age was not. Overall, breast cancer risk was substantially lower in breastfed women than formula-fed women, although the reduced risk was not apparent in the first-born women. The authors cite breastmilk contaminated with persistent organic pollutants (POPs) as a plausible mechanism to explain their findings. However, an equally plausible and not mutually exclusive hypothesis is that exposure to infant formula (or lack of human milk) is associated with increased risk of breast cancer. Toxic effects of POPs are orchestrated by arylhydrocarbon receptor (AhR) in the cell. AhR activation is the initial event of biologically significant exposures to POPs, which causes, among other things, cytochrome P4501A (CYP1A) induction, a biomarker of AhR activation. Therefore, we were surprised when we unexpectedly discovered that AhR is strongly activated, and CYP1A induced, in vitro by infant formula, but not by human milk.2 A recent study by Blake et al3 showed that CYP1A activity is significantly higher in formula-fed infants, consistent with our data.2 POPs are surely present in human milk, but the amount found in women may not be biologically meaningful. Breastmilk is still far better than infant formula under most circumstances for many other reasons. Because the findings by Nichols et al1 are important, it is even more important to be interpreted in a fair and balanced manner. Many studies including ours and Nichols’ have delved into the big black box. At least for now, the answers remain unclear. Shinya Ito Division of Clinical Pharmacology and Toxicology Hospital for Sick Children University of Toronto Toronto, Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".