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Record W2011721641 · doi:10.1016/s2214-109x(13)70170-0

Reassessing the value of vaccines

2014· article· en· W2011721641 on OpenAlexaff
Till Bärnighausen, Seth Berkley, Zulfiqar A Bhutta, David Bishai, Maureen M. Black, David E. Bloom, Dagna Constenla, Julia Driessen, John Edmunds, David Evans, Ulla Griffiths, Peter M. Hansen, Farah Naz Hashmani, Raymond Hutubessy, Dean T. Jamison, Prabhat Jha, Mark Jit, Hope L. Johnson, Ramanan Laxminarayan, Bruce Y. Lee, Sharmila Mhatre, Anne Mills, Anders Nordström, Sachiko Ozawa, Lisa A. Prosser, Karlee Silver, Christine Stabell Benn, Baudouin Standaert, Damian Walker

Bibliographic record

VenueThe Lancet Global Health · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsInternational Development Research CentreUniversity of Toronto
FundersWellcome TrustWorld Health Organization
KeywordsLife expectancyScopusImmunizationVaccinationGlobal healthPolitical scienceEconomic growthMedicineAlliancePublic healthMEDLINEPopulationEnvironmental healthImmunologyEconomics

Abstract

fetched live from OpenAlex

In May, 1974, WHO launched the Expanded Programme on Immunization—the global programme to immunise children worldwide with a set of (at the time) six core vaccines. 40 years on, the GAVI Alliance has brought us together, a group of 29 leading technical experts in health and development economics, cognitive development, epidemiology, disease burden, and economic modelling to review and understand the broader outcomes of vaccines beyond morbidity and mortality, to identify research opportunities, and to create a research agenda that will help to further quantify the value of this effect.

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)0.039
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.099
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.013
Scholarly communication0.0090.019
Open science0.0030.005
Research integrity0.0110.014
Insufficient payload (model declined to judge)0.0120.003

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 head0.037
GPT teacher head0.392
Teacher spread0.356 · 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 designNot applicable
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

Citations65
Published2014
Admission routes1
Has abstractyes

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