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Record W2149027553 · doi:10.1086/340280

The Changing Age and Seasonal Profile of Pertussis in Canada

2002· article· en· W2149027553 on OpenAlexaffabout
Danuta M. Skowronski, Gaston De Serres, Diane Macdonald, Wrency Wu, Carol Shaw, Jane Macnabb, Sylvie Champagne, David M. Patrick, Scott A. Halperin

Bibliographic record

VenueThe Journal of Infectious Diseases · 2002
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsIzaak Walton Killam Health CentreHealth CanadaInstitute of Population and Public HealthBC Centre for Disease ControlUniversity of British Columbia
Fundersnot available
KeywordsOutbreakIncidence (geometry)ImmunizationPopulationHerd immunityMedicinePediatricsPertussis vaccineDemographyImmunologyEnvironmental healthVirologyImmune system

Abstract

fetched live from OpenAlex

During the postvaccine era in Canada, most cases of pertussis have been reported in children <5 years of age, with the highest incidence, morbidity, and mortality in infants <1 year old. Population-based data, with very high laboratory confirmation rates and hospital separation and mortality statistics, chronicle the changing age and seasonal profile associated with pertussis over recent successive outbreaks in British Columbia, Canada. A large outbreak during 2000 highlights 2 important changes to the postvaccine profile. For the first time in Canada, the incidence of pertussis among preteens and teens surpassed that of all other age groups. At the same time, a decreasing incidence of pertussis among infants and preschool children highlights reduced susceptibility in the very young. Recent changes in the childhood immunization program (including introduction of an acellular pertussis vaccine), waning immunity, and changes in laboratory methods are considered in explaining these 2 simultaneous but divergent trends in the pertussis profile.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.200
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations233
Published2002
Admission routes2
Has abstractyes

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