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Record W2756403772 · doi:10.1093/infdis/jix476

The Impact of Human Papillomavirus Catch-Up Vaccination in Australia: Implications for Introduction of Multiple Age Cohort Vaccination and Postvaccination Data Interpretation

2017· article· en· W2756403772 on OpenAlexafffund
Mélanie Drolet, Jean‐François Laprise, Julia Brotherton, Basil Donovan, Christopher K. Fairley, Hammad Ali, Élodie Bénard, Dave Martin, Marc Brisson

Bibliographic record

VenueThe Journal of Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchSeqirusCanada Research ChairsGlaxoSmithKline
KeywordsVaccinationMedicineCervical cancerHuman papillomavirusDemographyCohortHerd immunityTransmission (telecommunications)Environmental healthHerdImmunologyCancerInternal medicineVeterinary medicineComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

We used transmission-dynamic modeling to estimate the added effectiveness of vaccinating multiple cohorts of females (12-26 years) in Australia compared with the theoretical introduction of routine-only (12-13 years) vaccination. Our results suggest that vaccinating multiple cohorts produced markedly faster direct/herd effects, and it added benefits that last for 20-70 years. Furthermore, the number needed to vaccinate to prevent 1 anogential warts (AGW) case or cervical cancer (CC) was similar for routine + catch-up (AGW = 9.9, CC = 678) and routine-only vaccination (AGW = 9.9, CC = 677), thus providing similar levels of efficiency per person vaccinated.

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)0.002
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 score0.077
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.059
GPT teacher head0.440
Teacher spread0.381 · 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

Citations45
Published2017
Admission routes2
Has abstractyes

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