Bibliographic record
Abstract
CONTEXT: Most individuals who react to peanuts do so on their first known exposure. A potential but unproven route of occult exposure resulting in sensitization to peanut is via breast milk during lactation. OBJECTIVE: To investigate the ability of maternal dietary peanut protein to pass into breast milk during lactation. DESIGN AND SETTING: Clinical investigation conducted at 2 North American hospitals from March 1999 to October 2000. PATIENTS: Twenty-three healthy, lactating women aged 21 to 35 years. INTERVENTION: Each woman consumed 50 g of dry roasted peanuts, after which breast milk samples were collected at hourly intervals. MAIN OUTCOME MEASURES: Presence in breast milk of total peanut protein, analyzed by a sandwich enzyme-linked immunosorbent assay, and 2 major peanut allergens, Ara h 1 and Ara h 2, detected by immunoblot analysis. RESULTS: Peanut protein was detected in 11 of 23 subjects. It was detected in 10 subjects within 2 hours of ingestion and in 1 subject within 6 hours. The median peak peanut protein concentration in breast milk was 200 ng/mL (mean, 222 ng/mL; range, 120-430 ng/mL). Both major peanut allergens Ara h 1 and Ara h 2 were detected. CONCLUSIONS: Peanut protein is secreted into breast milk of lactating women following maternal dietary ingestion. Exposure to peanut protein during breastfeeding is a route of occult exposure that may result in sensitization of at-risk infants.
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 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 | 0.000 | 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".