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Record W2149789275 · doi:10.1086/421404

Directly Observed Therapy for the Management of HIV-Infected Patients in a Methadone Program

2004· article· en· W2149789275 on OpenAlexaff
Brian Conway, Jennie Prasad, Robert J. Reynolds, John Farley, Michelle Jones, Salima Jutha, Nadine Smith, Annabel Mead, Stanley DeVlaming

Bibliographic record

VenueClinical Infectious Diseases · 2004
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSt. Paul's HospitalVancouver Coastal HealthUniversity of British Columbia
FundersNational Institute of Allergy and Infectious Diseases
KeywordsMedicineMethadoneHuman immunodeficiency virus (HIV)Intensive care medicineAntiretroviral therapySidaViral diseaseVirologyPsychiatryViral load

Abstract

fetched live from OpenAlex

The objective of this prospective, observational clinical study was to evaluate the safety and efficacy of once-daily and twice-daily directly observed therapy (DOT) in human immunodeficiency virus (HIV)-infected patients undergoing methadone treatment. Methadone and highly active antiretroviral therapy (HAART) were dispensed daily as DOT, with patients in the twice-daily HAART group self-administering the second dose. Clinical and laboratory end points were monitored, along with the impact of ongoing cocaine use. We studied 54 patients coinfected with HIV and hepatitis C virus. At baseline, the median virus load was 111,000 copies/mL, and the median CD4+ cell count was 165 cells/mm3. After a median of 24 months, 17 of 29 patients in the once-daily HAART group and 18 of 25 in the twice-daily HAART group had virus loads of <400 copies/mL, regardless of ongoing cocaine use. Thirty-two patients required methadone dose adjustment, which was managed without modification of HAART. Treatment-limiting hepatic toxicity was rare. A DOT program of coadministered methadone and HAART can be implemented with good results, even for patients who continue to use cocaine.

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)0.001
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 score0.267
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.082
GPT teacher head0.429
Teacher spread0.347 · 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

Citations88
Published2004
Admission routes1
Has abstractyes

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