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Record W2555832499 · doi:10.1016/j.ijid.2016.11.002

European policies in the management of tuberculosis among migrants

2016· article· en· W2555832499 on OpenAlexaboutno aff
Lia D’Ambrosio, Rosella Centis, Masoud Dara, Ivan Solovič, Giorgia Sulis, Alimuddin Zumla, Giovanni Battista Migliori

Bibliographic record

VenueInternational Journal of Infectious Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsEuropean unionRefugeeTuberculosisQuarter (Canadian coin)European regionIncidence (geometry)Political scienceEconomic growthDevelopment economicsMedicineGeographyEnvironmental healthBusinessInternational tradeEconomicsRegional science

Abstract

fetched live from OpenAlex

Globally 10.4 million new tuberculosis (TB) incident cases were estimated to have occurred in 2015, of which 3% were reported in the World Health Organization European Region. Importantly, about 25% of the global multidrug-resistant TB (MDR-TB) cases are reported in the European Region, representing one of the greatest challenges to TB control; these are reported particularly in the countries of the Former Soviet Union. Over a quarter of TB cases in the European Union and European Economic Area (EU/EEA) are reported among foreign-born individuals. In line with the recent increase of migration flows towards Europe, TB among migrant populations is also on the rise, emphasizing the need for a better understanding of the TB trends at the regional and sub-regional levels, and of the existing policies on migrants and refugees. The present article is aimed at describing the policies and practices of European countries with a low and intermediate TB incidence with regard to the detection and management of TB and latent TB infection (LTBI) among refugees in Europe.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.323
Teacher spread0.307 · 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.

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

Citations35
Published2016
Admission routes1
Has abstractyes

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