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Record W3199245732 · doi:10.1371/journal.pmed.1003702

Tuberculosis preventive treatment in people living with HIV—Is the glass half empty or half full?

2021· letter· en· W3199245732 on OpenAlexaff
Olivia Oxlade, Hannah Rochon, Jonathon R. Campbell, Dick Menzies

Bibliographic record

VenuePLoS Medicine · 2021
Typeletter
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University Health CentreMcGill University
FundersBill and Melinda Gates Foundation
KeywordsTuberculosisMedicineHuman immunodeficiency virus (HIV)EpidemiologyExtensively drug-resistant tuberculosisPublic healthImmunologyVirologyPediatricsMycobacterium tuberculosisInternal medicinePathology

Abstract

fetched live from OpenAlex

The most recent World Health Organization Global Tuberculosis (TB) Report suggests that 50% of people living with HIV (PLHIV) newly enrolled in HIV care initiated tuberculosis preventive treatment (TPT) in 2019 [1].TPT is an essential intervention to prevent TB disease among people infected with Mycobacterium tuberculosis-some 25% of the world's population [2].Without TPT, it is estimated that up to 10% of individuals will progress to TB disease.Among PLHIV, the prognosis is worse.Of the approximately 1.4 million annual deaths from TB, 200,000 occur among PLHIV [1], who experience TB at rates more than 30 times [3] higher than people living without HIV.In 2018, governments at the United Nations High-Level Meeting (UNHLM) on TB committed to rapid expansion of testing for TB infection and provision of TPT [4].The goal was the provision of TPT to at least 24 million household contacts of people with TB disease and 6 million PLHIV between 2018 and 2022.However, by the end of 2019, fewer than half a million household contacts had initiated TPT, well short of the pace needed to achieve the 5-year target [1].On the other hand, approximately 5.3 million PLHIV have initiated TPT in the past 2 years [1], with particularly dramatic increases in countries supported by the President's Emergency Plan for AIDS Relief (PEPFARAU : PleasenotethatPEPFARhasbeendefinedasPresident 0 sEmergen ) [5].Globally, among PLHIV entering HIV care programs, TPT initiation rose from 36% in 2017 to 49% in 2018 and 50% in 2019 [6,7].To provide insight into scaling up TPT for PLHIV, it is important to consider each of the many steps involved in the "cascade of care" for TPT.A previous systematic review of studies in several populations receiving TPT concluded that nearly 70% of all people who may benefit from TPT were lost to follow-up at cascade of care steps prior to treatment initiation [8].To maximize the impact of TPT for TB prevention among PLHIV, the full TPT cascade of care must be assessed to identify problems and develop targeted solutions addressing barriers at each step.Until now, these data had not been synthesized for PLHIV.In order to address important research gaps related to TPT in PLHIV such as this one, we are now presenting a Collection in PLOS Medicine on TPT in PLHIV.In the first paper in this Collection, Bastos and colleagues performed a systematic review and meta-analysis of the TPT cascade of care in 71 cohorts with a total of 94,011 PLHIV [9].AU : Anabbreviationlisthasbeencompiled This analysis highlights key steps in the cascade where substantial attrition occurs and identifies individual-level and programmatic barriers and facilitators at each step.In stratified analyses, they found that losses during the TPT cascade were not different in high-income compared to low-or middleincome settings, nor were losses greater in centers performing tests for TB infection (tuberculin skin test [TST] or interferon gamma release assay [IGRA]) prior to TPT initiation.

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)0.019
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.018
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0180.015
Insufficient payload (model declined to judge)0.0100.005

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 head0.042
GPT teacher head0.314
Teacher spread0.272 · 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
GenreCommentary

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

Citations8
Published2021
Admission routes1
Has abstractyes

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