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Record W2883635858 · doi:10.1016/s0140-6736(18)31442-9

New strategies for inpatients with HIV and tuberculosis

2018· letter· en· W2883635858 on OpenAlexaff
Ruvandhi R. Nathavitharana, Madhukar Pai

Bibliographic record

VenueThe Lancet · 2018
Typeletter
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersNational Institute of Allergy and Infectious DiseasesMedical Research CouncilWellcome Trust
KeywordsTuberculosisMedicineScopusSputumHuman immunodeficiency virus (HIV)Family medicineLipoarabinomannanPublic healthInternal medicineMEDLINEMycobacterium tuberculosisPathology

Abstract

fetched live from OpenAlex

Decreasing tuberculosis-related mortality—particularly in people living with HIV infection, who account for 22% of all tuberculosis deaths—is a pressing global health concern.1 Tuberculosis cascade-of-care analyses show that diagnosis is the weakest link.2,3 There is an urgent need for a non-sputum based, point-of-care diagnostic test for tuberculosis, particularly for groups such as those living with HIV infection who are often unable to produce sputum.4

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)0.003
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: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0450.005

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 head0.053
GPT teacher head0.333
Teacher spread0.280 · 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
GenreEditorial

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

Citations4
Published2018
Admission routes1
Has abstractyes

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