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Record W1993762071 · doi:10.1093/aje/153.7.653

Family Size, Day-Care Attendance, and Breastfeeding in Relation to the Incidence of Childhood Asthma

2001· article· en· W1993762071 on OpenAlexafffundabout
Claire Infante‐Rivard, Devendra Amre, Denyse Gautrin, Jean-Luc Malo

Bibliographic record

VenueAmerican Journal of Epidemiology · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsUniversité de MontréalMcGill University
FundersCanadian Lung Association
KeywordsMedicineBreastfeedingAsthmaOdds ratioPediatricsAttendanceAtopyConfidence intervalIncidence (geometry)Risk factorSiblingDemographyInternal medicine

Abstract

fetched live from OpenAlex

A hypothesis has been suggested stating that children exposed early to infections are less likely to develop atopy or asthma. The authors investigated the relation between risk of childhood asthma and number of siblings as well as day-care attendance, as factors possibly increasing the likelihood of early infections, and breastfeeding as a factor reducing them. A case-control study was carried out in Montréal, Canada, between 1988 and 1995 that included 457 children diagnosed with asthma at 3--4 years of age and 457 healthy controls. Cases followed for 6 years were later classified as persistent or transient by the symptoms and use of medication after diagnosis. Among cases diagnosed at 3--4 years of age, the adjusted odds ratio for asthma was 0.54 (95% confidence interval (CI): 0.36, 0.80) for one sibling and 0.49 (95% CI: 0.30, 0.81) for two or more. The adjusted odds ratio for day-care attendance before 1 year of age was 0.59 (95% CI: 0.40, 0.87). Results were similar with persistent cases. Among transient cases (who possibly had an infection with wheezing at 3--4 years of age), day-care attendance and a short duration of breastfeeding resulted in increased risk. The results support the hypothesis that opportunity for early infections reduces the risk of asthma.

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)0.004
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.400
Teacher spread0.364 · 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 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

Citations90
Published2001
Admission routes3
Has abstractyes

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