Bibliographic record
Abstract
We thank Hanauer and Choi (1) for their interest in our study (2). They are concerned about the positive association we reported between childhood leukemia and vitamin intake in childhood and would like clarification about what constituted a vitamin supplement in the study. In addition, they are wary of the potential public health implications related to our findings, particularly regarding clinical recommendations for vitamin D supplementation to prevent rickets in exclusively breastfed infants (3, 4). While maternal vitamin intake during pregnancy has been associated with a decreased risk of childhood leukemia (5), ours is one of the first studies to examine the effect of vitamin intake in infancy and childhood. We reported that children who received vitamin supplements during the first year of life or later were at an increased risk of leukemia (odds ratio = 1.66, 95% confidence interval: 1.18, 2.33) and acute lymphoblastic leukemia (odds ratio = 1.72, 95% confidence interval: 1.12, 2.44), yet that breastfeeding for more than 6 months conferred a protective effect. Unfortunately, the questionnaire used in our study did not discern between dietary supplements and true vitamin supplementation, nor did we collect information on the specific composition or intended uses of the vitamin supplement reported. As such, we cannot speculate on an association between vitamin D supplementation and childhood leukemia from our study findings.
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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 | 0.007 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.030 | 0.038 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".