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Record W4307841413 · doi:10.1093/jpids/piac070

Testing and Scaling Interventions to Improve the Tuberculosis Infection Care Cascade

2022· review· en· W4307841413 on OpenAlexaff
Jeffrey I. Campbell, Dick Menzies

Bibliographic record

VenueJournal of the Pediatric Infectious Diseases Society · 2022
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersAgency for Healthcare Research and Quality
KeywordsMedicineContact tracingTuberculosisPsychological interventionIntensive care medicineContinuum of careLatent tuberculosisInfection controlInfectious disease (medical specialty)DiseaseFamily medicineHealth careNursingMycobacterium tuberculosisCoronavirus disease 2019 (COVID-19)Pathology

Abstract

fetched live from OpenAlex

Tuberculosis (TB) preventive therapy (TPT) is increasingly recognized as the key to eliminating tuberculosis globally and is particularly critical for children with TB infection or who are in close contact with individuals with infectious TB. But many barriers currently impede successful scale-up to provide TPT to those at high risk of TB disease. The cascade of care in TB infection (and the related contact management cascade) is a conceptual framework to evaluate and improve the care of persons who are potential candidates for TPT. This review summarizes recent literature on barriers and solutions in the TB infection care cascade, focusing on children in both high- and low-burden settings, and drawing from studies on children and adults. Identifying and closing gaps in the care cascade will require the implementation of tools that are new (e.g. computer-assisted radiography) and old (e.g. efficient contact tracing), and will be aided by innovative implementation study designs, quality improvement methods, and shared clinical practice with primary care providers.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.870
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.050
GPT teacher head0.375
Teacher spread0.325 · 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 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

Citations17
Published2022
Admission routes1
Has abstractyes

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