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Record W1971155631 · doi:10.1159/000129623

The Impact of Vaccines in Low- and High-Income Countries

2008· article· en· W1971155631 on OpenAlexfundno aff
Leif Gothefors

Bibliographic record

VenueAnnales Nestlé (English ed ) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
FundersVector Institute
KeywordsMeaslesPoliomyelitisVaccinationMedicineEnvironmental healthDiphtheriaEconomic growthVaccine-preventable diseasesPolio vaccinePublic healthDeveloping countryOutbreakDevelopment economicsPediatricsVirologyEconomics

Abstract

fetched live from OpenAlex

Vaccination has become the most effective public health measure for the control of infectious diseases after the provision of clean drinking water. The history of vaccination is marked with great hopes and some disappointments. In particular, the second half of the 20th century witnessed the development of remarkable vaccination projects. There is a possibility that polio and measles may be eradicated within a few years, but almost 3 million people – usually children <5 years of age – die each year from diseases that are preventable by vaccines. Developing countries are struggling to get the vaccines to children who desperately need them. However, in Europe and North America, people have become complacent about vaccines: ‘these diseases are no longer a threat and the vaccine is more dangerous than the disease’. Those misconceptions have caused outbreaks of measles, diphtheria and pertussis. The international community must continue to devote the necessary resources, money and manpower to fully exploit the promise that vaccines hold for the relief of human misery.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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 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

Citations7
Published2008
Admission routes1
Has abstractyes

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