MétaCan
Menu
Back to cohort

Clinical and immunogenetic correlates of abacavir hypersensitivity

2005· article· en· W2032039529 on OpenAlexaff
Elizabeth J. Phillips, Gavin Wong, Rupert Kaul, Kamnoosh Shahabi, David Nolan, Simon R. Knowles, A. Martin, S. Mallal, Neil H. Shear

Bibliographic record

VenueAIDS · 2005
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsHealth Sciences CentreUniversity Health NetworkUniversity of TorontoSunnybrook Health Science CentreAIDS VancouverUniversity of British Columbia
Fundersnot available
KeywordsAbacavirCD8MedicineImmunologyHuman leukocyte antigenViral loadHuman immunodeficiency virus (HIV)Immune systemAntigenAntiretroviral therapy

Abstract

fetched live from OpenAlex

A patch test (PT) may be useful in defining true abacavir hypersensitivity syndrome (AHS). Seven previously PT-positive patients remote from the original AHS were shown to have robust 24 h responses, supporting PT durability. HLA-B*5701 was present in all seven PT-positive versus one of 11 controls tolerating abacavir (P < 0.001). Five of seven PT (71%) versus one of 11 controls (9%) (P = 0.005) showed significant abacavir-specific CD8 proliferation, suggesting a direct role for HLA-B*5701-restricted CD8 cells in the pathophysiology of AHS.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.325
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), 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

Citations198
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueAIDSSame topicDrug-Induced Adverse ReactionsFrench-language works237,207