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Canadian Experience With Implementation of an Acellular Pertussis Vaccine Booster-Dose Program in Adolescents: Implications for the United States

2005· article· en· W2020948627 on OpenAlexaffabout
Scott A. Halperin

Bibliographic record

VenueThe Pediatric Infectious Disease Journal · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health Centre
Fundersnot available
KeywordsDiphtheriaMedicineBooster (rocketry)ImmunizationBooster dosePertussis vaccineTetanusImmunization programEnvironmental healthPediatricsFamily medicineVaccinationImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: In Canada, the epidemiology of pertussis has changed during the past decade such that more cases occur in adolescents than in any other age cohort. METHODS: The implications of the Canadian experience, as well as the experiences of France, Germany, and Australia, on the universal implementation of an acellular pertussis, diphtheria, and tetanus booster vaccine in the United States are discussed. RESULTS: In 1999, an acellular pertussis vaccine combined with diphtheria and tetanus toxoids formulated for adolescents and adults was licensed for use in Canada. It has taken >5 years for this vaccine to be introduced universally into the immunization programs of all provinces and territories. The delay in implementation has likely been the result of insufficient epidemiologic data available to the National Advisory Committee on Immunization and the lack of a consensus on the appropriate goals of the national pertussis control strategy. Implementation of an immunization program in all parts of the country occurred only after a national consensus was achieved and federal funding was made available for vaccine purchase. CONCLUSIONS: The Canadian experience demonstrates that an adolescent pertussis vaccine program can be implemented on a national scale after several factors have been considered.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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

Citations46
Published2005
Admission routes2
Has abstractyes

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