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Record W1981725818 · doi:10.1056/nejmoa1405044

A 9-Valent HPV Vaccine against Infection and Intraepithelial Neoplasia in Women

2015· article· en· W1981725818 on OpenAlexaff
Elmar A. Joura, Anna R. Giuliano, Ole‐Erik Iversen, Céline Bouchard, Constance Mao, Jesper Mehlsen, Edson Duarte Moreira, Yuen Ngan, Lone Kjeld Petersen, Eduardo Lazcano‐Ponce, Punnee Pitisuttithum, Jaime Alberto Restrepo, Gavin Stuart, Linn Woelber, Yuh Cheng Yang, Jack Cuzick, Suzanne M. Garland, Warner K. Huh, Susanne K. Kjær, Oliver Bautista, Ivan S. F. Chan, Joshua Chen, Richard Gesser, Erin Moeller, Michael Ritter, Scott Vuocolo, Alain Luxembourg

Bibliographic record

VenueNew England Journal of Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of British ColumbiaUniversité Laval
FundersCancer Research UK
KeywordsCervical intraepithelial neoplasiaMedicineVirologyCervical cancerInternal medicineCancer

Abstract

fetched live from OpenAlex

BACKGROUND: The investigational 9-valent viruslike particle vaccine against human papillomavirus (HPV) includes the HPV types in the quadrivalent HPV (qHPV) vaccine (6, 11, 16, and 18) and five additional oncogenic types (31, 33, 45, 52, and 58). Here we present the results of a study of the efficacy and immunogenicity of the 9vHPV vaccine in women 16 to 26 years of age. METHODS: We performed a randomized, international, double-blind, phase 2b-3 study of the 9vHPV vaccine in 14,215 women. Participants received the 9vHPV vaccine or the qHPV vaccine in a series of three intramuscular injections on day 1 and at months 2 and 6. Serum was collected for analysis of antibody responses. Swabs of labial, vulvar, perineal, perianal, endocervical, and ectocervical tissue were obtained and used for HPV DNA testing, and liquid-based cytologic testing (Papanicolaou testing) was performed regularly. Tissue obtained by means of biopsy or as part of definitive therapy (including a loop electrosurgical excision procedure and conization) was tested for HPV. RESULTS: The rate of high-grade cervical, vulvar, or vaginal disease irrespective of HPV type (i.e., disease caused by HPV types included in the 9vHPV vaccine and those not included) in the modified intention-to-treat population (which included participants with and those without prevalent infection or disease) was 14.0 per 1000 person-years in both vaccine groups. The rate of high-grade cervical, vulvar, or vaginal disease related to HPV-31, 33, 45, 52, and 58 in a prespecified per-protocol efficacy population (susceptible population) was 0.1 per 1000 person-years in the 9vHPV group and 1.6 per 1000 person-years in the qHPV group (efficacy of the 9vHPV vaccine, 96.7%; 95% confidence interval, 80.9 to 99.8). Antibody responses to HPV-6, 11, 16, and 18 were noninferior to those generated by the qHPV vaccine. Adverse events related to injection site were more common in the 9vHPV group than in the qHPV group. CONCLUSIONS: The 9vHPV vaccine prevented infection and disease related to HPV-31, 33, 45, 52, and 58 in a susceptible population and generated an antibody response to HPV-6, 11, 16, and 18 that was noninferior to that generated by the qHPV vaccine. The 9vHPV vaccine did not prevent infection and disease related to HPV types beyond the nine types covered by the vaccine. (Funded by Merck; ClinicalTrials.gov number, NCT00543543).

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.031
GPT teacher head0.324
Teacher spread0.293 · 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 designRandomized trial
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

Citations1,420
Published2015
Admission routes1
Has abstractyes

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