MétaCan
Menu
Back to cohort
Record W2806561347 · doi:10.21037/jtd.2018.05.11

Management of drug-resistant tuberculosis in special sub-populations including those with HIV co-infection, pregnancy, diabetes, organ-specific dysfunction, and in the critically ill

2018· review· en· W2806561347 on OpenAlexaff
Aliasgar Esmail, Natasha F. Sabur, Ikechi G. Okpechi, Keertan Dheda

Bibliographic record

VenueJournal of Thoracic Disease · 2018
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsSt. Michael's HospitalWest Park Healthcare Centre
FundersMedical Research CouncilEuropean and Developing Countries Clinical Trials PartnershipSouth African Medical Research Council
KeywordsMedicineTuberculosisIntensive care medicineDrug resistant tuberculosisDrugDiseaseBedaquilinePregnancyCoinfectionHuman immunodeficiency virus (HIV)Drug resistanceDiabetes mellitusInfectious disease (medical specialty)LinezolidMycobacterium tuberculosisImmunologyInternal medicinePharmacologyPathology

Abstract

fetched live from OpenAlex

Tuberculosis remains a major problem globally, and is the leading cause of death from an infectious agent. Drug-resistant tuberculosis threatens to marginalise the substantial gains that have recently been made in the fight against tuberculosis. Drug-resistant TB has significant associated morbidity and a high mortality, with only half of all multidrug-resistant TB patients achieving a successful treatment outcome. Patients with drug-resistant TB in resource-poor settings are now gaining access to newer and repurposed anti-tuberculosis drugs such as bedaquiline, delamanid and linezolid. However, with ever increasing rates of co-morbidity, there is little guidance on how to manage complex patients with drug-resistant TB. We address that knowledge gap, and outline principles underpinning the management of drug-resistant TB in special situations including HIV co-infection, pregnancy, renal disease, liver disease, diabetes, and in the critically ill.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.620
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.401
Teacher spread0.332 · 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 designOther design
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

Citations45
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Thoracic DiseaseSame topicTuberculosis Research and EpidemiologyFrench-language works237,207