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Record W2599983260 · doi:10.1016/j.vaccine.2017.03.035

Assessment of sex-specific differences in adverse events following immunization reporting in Ontario, 2012–15

2017· article· en· W2599983260 on OpenAlexaffabout
Tara Harris, Jyotsna Nair, J Fediurek, Shelley L. Deeks

Bibliographic record

VenueVaccine · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsMedicineAdverse effectRelative riskPopulationPediatricsImmunizationDemographyConfidence intervalInternal medicineImmunologyEnvironmental healthAntibody

Abstract

fetched live from OpenAlex

We assessed sex-specific trends within passive vaccine safety surveillance in Ontario, Canada. AEFIs reported following vaccines administered between 2012 and 2015 were included. There were 2466 AEFI reports; 66.2% were female. Annualized reporting rates were 5.9 and 3.1 per 100,000 population, for females and males respectively. The female:male reporting rate ratio (RRR) was 1.9. Sex-specific differences by age group were greatest in adults 18-64years (RRR 6.3); whereas there were no differences in children <10years. Vaccine-specific RRRs were highest for vaccines recommended for routine use in adults or high risk populations. All event categories were female-predominant. The highest event-specific RRRs were for oculorespiratory syndrome (5.1), anaesthesia/paraesthesia (4.6) and anaphylaxis (3.0). Serious AEFIs (n=113) were more evenly distributed (57.5% female, RRR 1.3) than non-serious (66.6% female, RRR 1.9). AEFI reporting among females was consistently elevated within the passive surveillance system in Ontario. Further study of the relationship between sex/gender and AEFI reporting is needed.

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 score0.505
Threshold uncertainty score0.907

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 communication0.0000.001
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 head0.048
GPT teacher head0.337
Teacher spread0.289 · 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

Citations76
Published2017
Admission routes2
Has abstractyes

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