Protocol design for the ACTIVATE clinical trial: Exposure to vaginal microbiome in cesarean-delivered infants at high risk for allergies
Bibliographic record
Abstract
Background: Food allergy is increasingly common in the United States. Studies suggest that rising cesarean delivery rates are associated with many immune disorders, including allergic diseases. A preceding proof-of-concept study showed that the microbiota of infants born by cesarean delivery could be partially restored via vaginal microbiome exposure at birth via "vaginal seeding". Objectives: Described here is the design of a clinical trial to evaluate the effects of vaginal seeding in infants born by cesarean delivery on food allergen sensitization (egg, milk, and peanut) at 12 months of age. Methods: This study is supported by the Immune Tolerance Network in collaboration with the National Institute of Allergy and Infectious Diseases. ACTIVATE is a single-center, randomized, double-blind, placebo-controlled trial enrolling pregnant women and their newborns who have a first-degree relative with atopic disease (NCT03567707). Forty infants born vaginally and 80 infants born by cesarean delivery, randomized 1:1 to receive vaginal or placebo seeding, will be enrolled. Families are followed for 1 year, with an option to extend the follow-up for a total of 3 years. Results: The study is currently underway with an enrollment goal of 120 mother-infant pairs. Surveys and samples are collected from mothers and infants during the follow-up period including blood, stool, skin swabs, oral swabs, nasal swabs, maternal vaginal swabs, and breast milk. Conclusions: This pilot study will provide important data on the effects of vaginal seeding on allergen sensitization, the microbiome, and the development of immune responses in the first 3 years of life.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.105 | 0.020 |
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".