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Record W2918060677 · doi:10.5588/ijtld.18.0866

Health care gaps in the global burden of drug-resistant tuberculosis

2019· review· en· W2918060677 on OpenAlexaff
Vivian Cox, Helen Cox, Madhukar Pai, Jonathan Stillo, Brian Citro, Grania Brigden

Bibliographic record

VenueThe International Journal of Tuberculosis and Lung Disease · 2019
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersInternational Union Against Tuberculosis and Lung DiseaseWellcome Trust
KeywordsMedicineTuberculosisPovertyExtensively drug-resistant tuberculosisHealth careGlobal healthBurden of diseaseHealthcare systemEnvironmental healthIntensive care medicinePublic healthEconomic growthMycobacterium tuberculosisNursingPathology

Abstract

fetched live from OpenAlex

The drug-resistant tuberculosis (DR-TB) cascade-from estimated or incident cases to numbers successfully treated or disease-free survival-has long been characterised by sharp declines at each step in the cascade. The losses along the cascade vary across different settings, and the reasons why some countries have a higher burden of DR-TB are complex and multifactorial; broadly, weak health systems, inadequate financing and poverty all impact differential access to DR-TB care. Within a human rights framework that mandates the right to health and the right to benefit from scientific progress, the aim of this review is to focus on describing inequities in access to DR-TB care at critical points in the cascade.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.396
Teacher spread0.367 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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