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Record W2161406887 · doi:10.1001/archpedi.161.12.1176

Hepatitis B Vaccination and the Risk of Childhood-Onset Multiple Sclerosis

2007· article· en· W2161406887 on OpenAlexaff
Yann Mikaeloff

Bibliographic record

VenueArchives of Pediatrics and Adolescent Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsRoyal Victoria HospitalMcGill University
Fundersnot available
KeywordsMedicineVaccinationOdds ratioPediatricsConfidence intervalHepatitis B vaccinePopulationHepatitis BMultiple sclerosisDemographyHepatitis AHepatitisImmunologyInternal medicineHepatitis B virusEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate whether vaccination against hepatitis B (HB) increases the risk of incident multiple sclerosis (MS) in childhood in the short and long terms. DESIGN: Case-control study. SETTING: Population-based study conducted in France from January 1, 1994, to December 31, 2003. PARTICIPANTS: The case patients had incident MS with onset before age 16 years. Each case was individually matched for age, sex, and geographic location (current place of residence) to 12 control participants randomly selected from the general population of France. EXPOSURE: Hepatitis B vaccine. MAIN OUTCOME MEASURE: The risk of MS associated with HB vaccine exposure. RESULTS: One hundred forty-three case patients with MS were matched to 1122 control participants. The rate of HB vaccination in the 3 years before the index date was approximately 32% for both cases and controls. Vaccination against HB within the 3-year study period was not associated with an increased rate of a first episode of MS (adjusted odds ratio, 1.03; 95% confidence interval, 0.62-1.69). The rate was also not increased for HB vaccination within 6 months of the index date or at any time since birth or as a function of the number of injections or the brand of HB vaccine. CONCLUSION: Vaccination against HB does not seem to increase the risk of a first episode of MS in 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)0.003
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 score0.155
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.266
Teacher spread0.245 · 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

Citations94
Published2007
Admission routes1
Has abstractyes

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