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Impact of HAART and injection drug use on life expectancy of two HIV-positive cohorts in British Columbia

2006· article· en· W2006489635 on OpenAlexafffundabout
Elisa Lloyd‐Smith, Elizabeth Brodkin, Evan Wood, Thomas Kerr, Mark Tyndall, Julio Montaner, Robert S. Hogg

Bibliographic record

VenueAIDS · 2006
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British ColumbiaSt. Paul's HospitalAIDS Vancouver
FundersMichael Smith Health Research BC
KeywordsLife expectancyHuman immunodeficiency virus (HIV)MedicineDrugSidaCohort studyLentivirusCohortVirologyViral diseaseDemographyGerontologyInternal medicineEnvironmental healthPopulationPharmacologySociology

Abstract

fetched live from OpenAlex

BACKGROUND: The introduction of HAART has led to consistent improvements in survival among HIV-infected individuals. However, there is evidence that not all populations have benefited equally from HAART and that mortality rates are higher in HIV-infected injection drug users than in non-users. OBJECTIVE: To model life expectancies for HIV-positive individuals subdivided according to history of injection drug use and treatment with HAART. DESIGN: Population-based study of HIV-positive persons in British Columbia's HIV/AIDS treatment program. METHODS: The primary outcome measures in this study were life expectancy at exact age 20 and potential years of life lost. RESULTS: The highest life expectancy (38.9 years) and lowest potential years of life lost were measured for individuals taking HAART and without a history of injection drug use. The lowest life expectancy (19.1 years) and highest potential years of life lost were measured in HIV-positive injection drug users who were not taking HAART. CONCLUSIONS: There are substantial disparities in life expectancy for persons living with HIV in British Columbia. Members of the injection drug community, particularly those who are not taking HAART, experience elevated mortality in comparison with those without a history of drug use.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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 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

Citations70
Published2006
Admission routes3
Has abstractyes

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