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Record W2925159988 · doi:10.1016/s0140-6736(19)30024-8

Building a tuberculosis-free world: The Lancet Commission on tuberculosis

2019· review· en· W2925159988 on OpenAlexaff
Michael Reid, Nimalan Arinaminpathy, Amy Bloom, Barry R. Bloom, Catharina Boehme, Richard E. Chaisson, Daniel P. Chin, Gavin Churchyard, Helen Cox, Lucica Diţiu, Mark Dybul, Jeremy Farrar, Anthony S. Fauci, Endalkachew Fekadu, Paula I. Fujiwara, Timothy B. Hallett, Christy Hanson, Mark Harrington, Nick Herbert, Philip C. Hopewell, Chieko Ikeda, Dean T. Jamison, Aamir Khan, Irene Koek, Nalini Krishnan, Aaron Motsoaledi, Madhukar Pai, Mario Raviǵlione, Almaz Sharman, Peter M. Small, Soumya Swaminathan, Zelalem Temesgen, Anna Vassall, Nandita Venkatesan, Kitty Van Weezenbeek, Gavin Yamey, Bruce Agins, Sofia Alexandru, Jason R. Andrews, Naomi Beyeler, Stela Bivol, Grania Brigden, Adithya Cattamanchi, Danielle Cazabon, Valeriu Crudu, Amrita Daftary, Puneet Dewan, Laurie Doepel, Robert W. Eisinger, Victoria Y. Fan, Sara Fewer, Jennifer Furin, Jeremy D. Goldhaber‐Fiebert, Gabriela B. Gomez, Stephen M. Graham, Devesh Gupta, Maureen Kamene, Sunil Khaparde, Eunice Mailu, Enos Masini, Lorrie McHugh, Ellen M.H. Mitchell, Suerie Moon, Michael Osberg, Tripti Pande, Lea Prince, Kiran Rade, Raghuram Rao, Michelle Remme, James A. Seddon, Casey Selwyn, Priya B. Shete, Kuldeep Singh Sachdeva, Guy Stallworthy, Juan F Vesga, Valentina Vilc, Eric Goosby

Bibliographic record

VenueThe Lancet · 2019
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersWellcome TrustWorld Health OrganizationMedical Research CouncilUnited States Agency for International Development
KeywordsTuberculosisMedicineGlobal healthDiseasePandemicInfectious disease (medical specialty)Development economicsEconomic growthPublic healthCoronavirus disease 2019 (COVID-19)Economics

Abstract

fetched live from OpenAlex

Tuberculosis can be treated, prevented, and cured. Rapid, sustained declines in tuberculosis deaths in many countries during the past 50 years provide compelling evidence that ending the pandemic is feasible. Yet this disease—which has plagued humanity since before recorded history and has killed hundreds of millions of people over the past two centuries—remains a relentless scourge. In 2017, 1·6 million people died from tuberculosis, including 300 000 people with HIV, representing more deaths than any other infectious disease.

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)0.005
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0190.006

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 head0.156
GPT teacher head0.430
Teacher spread0.273 · 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
GenreReview

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

Citations369
Published2019
Admission routes1
Has abstractyes

Explore more

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