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Record W1966493000 · doi:10.1182/blood-2005-11-4407

Mortality rates and causes of death among all HIV-positive individuals with hemophilia in Canada over 21 years of follow-up

2006· article· en· W1966493000 on OpenAlexafffundabout
Donald M. Arnold, Jim A. Julian, Irwin Walker

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCanadian Hemophilia Society
FundersMcMaster University
KeywordsMedicineSeroconversionCohortHepatitis CConfidence intervalSurvival analysisMortality rateLiver diseaseViral diseaseInternal medicineCause of deathPopulationImmunologyDemographyHuman immunodeficiency virus (HIV)DiseaseEnvironmental health

Abstract

fetched live from OpenAlex

Many individuals with hemophilia were infected with human immunodeficiency virus (HIV) in the early 1980s through contaminated blood products. Most also were co-infected with hepatitis C virus (HCV). Deaths among the entire cohort of HIV-positive hemophiliacs in Canada up to 2003 are described. Using registry data, we analyzed Kaplan-Meier survival curves, determined the effect of age at HIV seroconversion on mortality, and described cause-specific proportional mortality patterns over time. Of 2427 Canadians with hemophilia, 660 (27.2%) were HIV-positive, of whom 406 (61.5%) died. In contrast, 114 (6.5%) deaths occurred in HIV-negative controls. Median age at HIV seroconversion was 20 (range, < 1-67 years), and median survival was 15.0 years (95% confidence interval, 13.6-16.4 years). Younger age at HIV seroconversion was associated with improved survival; however, this finding was not explained by differences in causes of death across age groups. Following the introduction of highly active antiretroviral therapy, the proportion of deaths due to acquired immune deficiency syndrome has decreased, while the proportion of deaths due to liver disease has increased. There were 1134 HCV-positive individuals, of whom only 444 (39.2%) were also HIV-positive. Liver disease is a growing health concern among many hemophiliacs, not only those who are HIV-positive.

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 score0.049
Threshold uncertainty score0.240

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 head0.281
Teacher spread0.267 · 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

Citations73
Published2006
Admission routes3
Has abstractyes

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