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Record W1963548752 · doi:10.1097/aci.0b013e328349b166

Early life exposures

2011· review· en· W1963548752 on OpenAlexaff
Anita L. Kozyrskyj, Salma Bahreinian, Meghan B. Azad

Bibliographic record

VenueCurrent Opinion in Allergy and Clinical Immunology · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAsthmaDiseaseAllergyImmunologyVitamin D and neurologyEnvironmental healthImmune systemPregnancyBiologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To identify and discuss recent studies relating prenatal and early-life environmental exposures to the development of asthma and allergic disease. RECENT FINDINGS: New studies show that prenatal and early-life stress can alter infant immune profiles, increasing risk for asthma and allergy. Mounting evidence implicates indoor and outdoor air pollution in the origins of allergic disease, while Vitamin D intake and a Mediterranean diet may be protective. The role of early-life fever and infection remain controversial, with recent studies yielding conflicting results and new evidence indicating that previous studies may have been confounded. New studies are increasingly focused on environmental 'imprinting' of the infant gut microbiota, which is a critical determinant of immune system development. Early exposures impacting the intestinal microbiota include mode of delivery, infant diet, and use of antibiotics - factors that are also associated with childhood asthma and allergic disease. SUMMARY: This overview highlights environmental exposures during the in-utero and ex-utero time periods that are potential stimuli for the early programming of asthma and allergy. Special consideration is given for the potential role of intestinal microbiota. Future studies in this field promise to inform health policy and intervention strategies for the prevention of asthma and allergic disease.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.152
GPT teacher head0.438
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations117
Published2011
Admission routes1
Has abstractyes

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