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Record W2181045806 · doi:10.1093/cid/civ839

Measles in the 21st Century, a Continuing Preventable Risk to Travelers: Data From the GeoSentinel Global Network

2015· article· en· W2181045806 on OpenAlexaff
Mark J. Sotir, Douglas H. Esposito, Elizabeth D. Barnett, Karin Leder, Phyllis E. Kozarsky, Poh Lian Lim, Effrossyni Gkrania‐Klotsas, Davidson H. Hamer, Susan Kuhn, Bradley A. Connor, Rashila Pradhan, Éric Caumes

Bibliographic record

VenueClinical Infectious Diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersSanofi PasteurU.S. Public Health ServiceCenters for Disease Control and PreventionNational Institutes of HealthInternational Society of Travel MedicineNational Institute for Health and Care ResearchGilead SciencesSanofi
KeywordsMeaslesMedicineVaccinationMeasles vaccineEnvironmental healthTravel medicineVirology

Abstract

fetched live from OpenAlex

Measles remains a risk for travelers, with 94 measles diagnoses reported to the GeoSentinel network from 2000 to 2014, two-thirds since 2010. Asia was the most common exposure region, then Africa and Europe. Efforts to reduce travel-associated measles should target all vaccine-eligible travelers, including catch-up vaccination of susceptible adults.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.134
GPT teacher head0.426
Teacher spread0.292 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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