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Record W3034299338 · doi:10.1080/21645515.2020.1765619

Close the gap for routine mumps vaccination in Japan

2020· review· en· W3034299338 on OpenAlexaff
Taito Kitano

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2020
Typereview
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineAseptic meningitisVaccinationMumps vaccineDisease burdenEpidemiologyEnvironmental healthPopulationMeningitisPediatricsIncidence (geometry)ImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Mumps is a vaccine-preventable disease. Because the mumps vaccine can cause aseptic meningitis in rare cases, this vaccine is not routine in Japan. This has led to low vaccine coverage and severe disease burden in Japan. The present review summarizes mumps epidemiology and vaccination and discusses effective future strategies to mitigate the current disease burden of mumps in Japan. Although a recent study reported that mumps vaccine coverage rates are improving in Japan, current coverage rates are far below the optimal rate to suppress the ongoing epidemic, which has caused an average annual financial loss of 85 billion JPY between 2000 and 2016. Recent reports have demonstrated a much lower incidence of vaccine-induced aseptic meningitis in newly developed vaccines, especially when administered at 1 year of age. Cost-effectiveness studies suggest that routinization of the currently distributed domestic vaccine would be highly cost-effective. In addition, questionnaire surveillance data suggest that the majority of the Japanese population accepts the nominal risk of the vaccine when the proper information is provided. Finally, there are some successful programs in Japan that have attained high vaccine coverage rates with financial support from local governments. Taken together, these data suggest that the mumps vaccine should be immediately included in routine vaccines in Japan.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.119
GPT teacher head0.413
Teacher spread0.294 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2020
Admission routes1
Has abstractyes

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