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Record W2955777027 · doi:10.1377/hlthaff.2018.05391

Twenty Years Of Antiretroviral Therapy For People Living With HIV: Global Costs, Health Achievements, Economic Benefits

2019· article· en· W2955777027 on OpenAlexaff
Steven Forsythe, William Paul McGreevey, Alan Whiteside, Maunank Shah, Joshua Cohen, Robert Hecht, Lori Bollinger, Anthony Kinghorn

Bibliographic record

VenueHealth Affairs · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsWilfrid Laurier UniversityCentre for International Governance Innovation
Fundersnot available
KeywordsAntiretroviral therapyMedicineGlobal healthHuman immunodeficiency virus (HIV)Economic growthAntiretroviral treatmentDeveloping countryEnvironmental healthScale (ratio)GerontologyPublic healthGeographyFamily medicineEconomicsViral loadNursing

Abstract

fetched live from OpenAlex

Since the introduction of azidothymidine in 1987, significant improvements in treatment for people living with HIV have yielded substantial improvements in global health as a result of the unique benefits of antiretroviral therapy (ART). ART averted 9.5 million deaths worldwide in 1995-2015, with global economic benefits of $1.05 trillion. For every $1 spent on ART, $3.50 in benefits accrued globally. If treatment scale-up achieves the global 90-90-90 targets of the Joint United Nations Programme on HIV/AIDS, a total of 34.9 million deaths are projected to be averted between 1995 and 2030. Approximately 40.2 million new HIV infections could also be averted by ART, and economic gains could reach $4.02 trillion in 2030. Having provided ART to 19.5 million people represents a major human achievement. However, 15.2 million infected people are currently not receiving treatment, which represents a significant lost opportunity. Further treatment scale-up could yield even greater health and economic benefits.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.003

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 head0.018
GPT teacher head0.329
Teacher spread0.311 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations119
Published2019
Admission routes1
Has abstractyes

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