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Record W2103540770 · doi:10.1093/jnci/djt060

RE: Population-Level Impact of the Bivalent, Quadrivalent, and Candidate Nonavalent Human Papillomavirus Vaccines: A Comparative Model-Based Analysis

2013· letter· en· W2103540770 on OpenAlexaff
Eric J. Suba, Ludwig González-Mena, Stephen S. Raab

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2013
Typeletter
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBivalent (engine)Human papillomavirus vaccineHuman papillomavirusVirologyMedicineCervical cancerInternal medicineChemistryGardasilCancer

Abstract

fetched live from OpenAlex

In a recent article in the Journal, Van de Velde et al. ( 1 ) assume that human papillomavirus (HPV) vaccines “prevent infection but do not alter the natural history of disease in individuals already infected by a vaccine type.” We question whether this assumption is correct. In 2006, the US Food and Drug Administration (FDA) expressed concerns about “the potential for Gardasil to enhance disease among a subgroup of subjects who had evidence of persistent infection with vaccine-relevant HPV types at baseline” ( 2 ). The increase in high-grade cervical disease rates among a subgroup of vaccinated girls and women noted by the FDA was not statistically significant. However, a subsequent ecological study of approximately 2.9 million Australian girls and women ( 3 ) documented that the introduction of Gardasil was associated with statistically significant increases in high-grade cervical disease rates among women aged more than 21 years but with statistically significant decreases in high-grade cervical disease rates among girls and women aged less than 18 years. Decreases in high-grade cervical disease rates among Australian girls and women aged less than 18 years were considered evidence of beneficial effect from Australia’s HPV vaccination program ( 3 ). However, to our knowledge, no explanations have been offered for the increases in high-grade cervical disease rates reported from the same study among Australian women aged more than 21 years. We ask Van de Velde et al. to consider whether the ecological observations from Australia suggest that Gardasil may in fact be enhancing disease among a subgroup of vaccinated individuals. In an accompanying editorial, Sahasra buddhe and Sherman ( 4 ) claim that “HPV vaccination provides the scientific and public health community an unprecedented opportunity to reduce the burden of cervical cancer.” We question the scientific accuracy of this claim. Should perfect HPV vaccine efficacy last less than 15 to 20 years, HPV vaccination will prove to have been a “costly failed public health experiment in cancer control” ( 5 ). The best-case scenario modelled by Van de Velde et al., which includes an assumption of lifelong, perfect HPV vaccine efficacy, predicts that HPV vaccination will reduce cervical cancer rates by approximately 30% over 70 years ( 1 ). In contrast, the US Preventive Services Task Force has determined that Papanicolaou cytology screening reduces cervical cancer rates by 60% to 90% within 3 years of its introduction to populations naive to screening and that these reductions of disease burden are “‘consistent and equally dramatic across populations”‘ ( 6 ). Even if HPV vaccines eventually prove to confer lifelong perfect efficacy, we question whether the introduction of HPV vaccines to resource-constrained settings will decelerate coverage of target demographic groups by cervical screening services and thereby decelerate rather than accelerate global reductions in cervical cancer–related mortality ( 7 ).

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)0.015
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.052
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.188
GPT teacher head0.450
Teacher spread0.262 · 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 designSimulation or modeling
Domainnot available
GenreCommentary

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

Citations4
Published2013
Admission routes1
Has abstractno

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