MétaCan
Menu
Back to cohort
Record W2130447065 · doi:10.1086/376835

Antiretroviral Concentrations in Untimed Plasma Samples Predict Therapy Outcome in a Population with Advanced Disease

2003· article· en· W2130447065 on OpenAlexaffabout
Christopher S. Alexander, Jérôme J. Asselin, Lillian Ting, Julio Montaner, Robert S. Hogg, Benita Yip, Michael V. O’Shaughnessy, P. Richard Harrigan

Bibliographic record

VenueThe Journal of Infectious Diseases · 2003
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsUniversity of British ColumbiaAIDS Vancouver
Fundersnot available
KeywordsMedicineInternal medicineAntiretroviral therapyProtease inhibitor (pharmacology)Reverse-transcriptase inhibitorViral loadPopulationMedical prescriptionSidaSalvage therapyRetrospective cohort studyDrugHuman immunodeficiency virus (HIV)ImmunologyOncologyViral diseaseGastroenterologyPharmacologyChemotherapy

Abstract

fetched live from OpenAlex

This study was designed to examine the relationship between untimed antiretroviral concentrations measured in plasma samples collected for virus-load testing and response to highly active antiretroviral therapy. Plasma nonnucleoside reverse-transcriptase-inhibitor and protease-inhibitor concentrations were retrospectively measured in all virus-load plasma samples collected during the first year of therapy, for 122 patients in British Columbia, Canada, who initiated therapy between August 1996 and September 1999 and who had CD4 counts <50 cells/micro L. Drug levels were designated a priori as "low" if the concentrations were below the published Ctrough-SD. A single low drug level measured shortly after initiation of therapy (median, 6 weeks) is common (30%) and is predictive of both more-rapid immunological failure (P=.06) and failure to achieve virologic success during the first year of therapy (P=.01). These results may reflect incomplete adherence, since a strong association (P<.001) was found between low drug levels and an imperfect prescription-refill record (<95%).

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 score0.007
Threshold uncertainty score0.342

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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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

Citations56
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Infectious DiseasesSame topicHIV/AIDS drug development and treatmentFrench-language works237,207