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Record W2126701220 · doi:10.1183/09031936.00115010

Farming in childhood, diet in adulthood and asthma history

2011· article· en· W2126701220 on OpenAlexaff
Raphaëlle Varraso, Marie‐Pierre Oryszczyn, Natalia Romero Mathieu, Nicole Le Moual, M.-C. Boutron-Ruault, Françoise Clavel‐Chapelon, Isabelle Romieu, F. Kauffmann

Bibliographic record

VenueEuropean Respiratory Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
FundersInstitut National de la Santé et de la Recherche Médicale
KeywordsAsthmaAgricultureMedicineEarly adulthoodDemographyEarly childhoodYoung adultLivestockEnvironmental healthPediatricsGerontologyGeographyPsychologyDevelopmental psychologyImmunology

Abstract

fetched live from OpenAlex

The decrease in the number of children living on traditional farms in France during early childhood and changes in diet could both play a role in the increase in asthma prevalence over the last decades. This study aimed to assess 1) the association of farming lifestyle in childhood and asthma, and 2) whether diet in adulthood modifies the association between farming lifestyle in childhood and adult-onset asthma. In the French Etude Epidemiologique des Femmes de la Mutuelle Générale de l'Education Nationale (E3N) study (54,018 females; age 43-68 yrs), three indicators of farming lifestyle were defined: one using individual data (having farmer parents) and two using ecological data (born in a rural area and exposure to cattle). All farming lifestyle indicators were related to childhood- (<16 yrs) and adult-onset asthma (OR (95% CI) values for farmer parents were 0.54 (0.42-0.70) and 0.72 (0.62-0.84), respectively), and to diet in adulthood, in particular to high fruit and low wine intakes. The association between farmer parents and adult-onset asthma was not modified by diet in adulthood. Results extend previous observations in younger cohorts on the protective role of contact with livestock and farming lifestyle on asthma, in particular during childhood.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.251
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2011
Admission routes1
Has abstractyes

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