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Record W2156570438 · doi:10.1177/1545109709333081

Hematologic Changes Associated With Zidovudine Following Single-Drug Substitution From Stavudine in a Home-Based AIDS Care Program in Rural Uganda

2009· article· en· W2156570438 on OpenAlexaff
Fatu Forna, David Moore, Jonathan Mermin, John T. Brooks, Willy Were, Kate Buchacz, James D. Campbell, Robert Downing, Craig B. Borkowf, Paul J. Weidle

Bibliographic record

VenueJournal of the International Association of Physicians in AIDS Care · 2009
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsAIDS Vancouver
Fundersnot available
KeywordsLeukopeniaZidovudineStavudineAnemiaMedicineIncidence (geometry)Internal medicinePediatricsGastroenterologyImmunologyChemotherapyHuman immunodeficiency virus (HIV)Viral disease

Abstract

fetched live from OpenAlex

BACKGROUND: The authors evaluated hematologic changes associated with zidovudine (ZDV) following single-drug substitution from stavudine (D4T) in HIV-infected persons in Uganda. METHODS: From May 2003 through February 2007, the authors evaluated incidence rates (IR) of hematologic abnormalities from quarterly blood draws among adults prescribed highly active antiretroviral therapy (HAART) before and after single-drug substitution of D4T to ZDV. RESULTS: A total of 1089 adults received D4T-containing HAART (median observation time, 35.9 months), and 290 (27%) had ZDV substituted for D4T. While taking D4T, IR for anemia was 0.35/100 person-months (PMs), leukopenia was 0.29/100 PM, and thrombocytopenia was 0.32/100 PM. While taking ZDV, IR for anemia was 0.44/100 PM, leukopenia was 1.05/100 PM, and thrombocytopenia was 0.30/100 PM. CONCLUSIONS: Patients had a higher incidence of anemia and leukopenia after substitution from D4T to ZDV, but hematologic toxicity was not a major complication in this population. Patients on ZDV-containing HAART regimens are still at risk for anemia and need close monitoring.

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 score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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

Citations27
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the International Association of Physicians in AIDS CareSame topicHIV/AIDS drug development and treatmentFrench-language works237,207