Record W7057029642

Global, regional, and national burden of tuberculosis, 1990-2016: results from the Global Burden of Diseases, Injuries, and Risk Factors 2016 Study

2018· article· en· W7057029642 on OpenAlexfundno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2018

Typearticle

Languageen

FieldPhysics and Astronomy

TopicMagnetic confinement fusion research

Canadian institutionsnot available

FundersErasmus Universitair Medisch Centrum RotterdamResearch Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesDebre Tabor UniversityNIH Clinical CenterDebre Markos UniversityUniversitair Ziekenhuis AntwerpenAlfaisal UniversityApplied Molecular Biosciences UnitLaboratório Associado para a Química VerdeFrankfurt University of Applied SciencesUniversity of NamibiaWestern Sydney UniversityLorestan University of Medical SciencesHawassa UniversityRede de Química e TecnologiaNational Center of Neurology and PsychiatryInternational Medical UniversityXiamen UniversityUniversity of HailAddis Ababa UniversityUniversity of GondarUniversitatea de Medicină şi Farmacie "Carol Davila" BucureştiNational Research University Higher School of EconomicsUniversidade de São PauloAhvaz Jundishapur University of Medical SciencesTaipei Medical UniversityTampereen YliopistoTehran University of Medical Sciences and Health ServicesGeorge Mason UniversityMazandaran University of Medical SciencesJordan University of Science and TechnologyInvasive Fungi Research Center, Mazandaran University of Medical SciencesSeoul National UniversityUniversidade do PortoShahid Beheshti University of Medical SciencesBundesministerium für GesundheitUniversity of TorontoAlborz University of Medical SciencesAin Shams UniversityMcGill UniversityPublic Health AgencyUniversität BielefeldPublic Health Foundation of IndiaUniversity College LondonUniversity of WarwickKyung Hee UniversityOhio State UniversityAhmadu Bello UniversityUniversitas Negeri SemarangIslamic Azad UniversityMaragheh University of Medical SciencesKarolinska InstitutetUnited Nations Population FundTrường Đại học Nguyễn Tất ThànhTrường Đại học Duy TânIran University of Medical SciencesUniversity of PeradeniyaUniversity of the PhilippinesMekelle UniversityUniversity of Massachusetts BostonUniversità degli Studi di SassariBill and Melinda Gates FoundationWest Virginia UniversityHarvard UniversitySan Diego State UniversityPublic Health Agency of CanadaJimma UniversityHaramaya UniversityMonash UniversityJazan UniversityUniversity of PittsburghDilla UniversitySanjay Gandhi Postgraduate Institute of Medical SciencesJohns Hopkins University

KeywordsTuberculosisVerbal autopsyIncidence (geometry)Mortality ratePublic healthGlobal healthEpidemiologyCause of deathDisease burden

DOInot available

Abstract

fetched live from OpenAlex

No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003

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 score0.145

Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

Category	Codex	Gemma
Metaresearch	0.001	0.003
Meta-epidemiology (narrow)	0.001	0.001
Meta-epidemiology (broad)	0.001	0.003
Bibliometrics	0.001	0.004
Science and technology studies	0.000	0.000
Scholarly communication	0.001	0.001
Open science	0.001	0.002
Research integrity	0.001	0.001
Insufficient payload (model declined to judge)	0.001	0.000

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 head0.019

GPT teacher head0.263

Teacher spread0.245 · 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".

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Citations0

Published2018

Admission routes1

Has abstractno

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