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Record W1971795611 · doi:10.1097/qad.0b013e3283398294

The impact of CCL3L1 copy number in an HIV-1-infected white population

2010· article· en· W1971795611 on OpenAlexaff
Erika Y. Lee, Feng Yun Yue, R. Brad Jones, Calvin Lo, Prameet M. Sheth, Martin Hyrcza, Colin Kovacs, Erika Benko, Rupert Kaul, Mario Ostrowski

Bibliographic record

VenueAIDS · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of TorontoSt. Michael's HospitalMaple Leaf Medical Clinic
Fundersnot available
KeywordsBiologyPopulationLentivirusViral loadViral diseaseImmunologyVirologyCohortCopy-number variationHuman immunodeficiency virus (HIV)GeneticsGeneDemographyMedicineInternal medicineGenome

Abstract

fetched live from OpenAlex

We examined the effect of CCL3L1 gene copy number on disease progression in a North American white cohort of HIV-1-infected individuals. Although CCL3L1 copy number is enriched in uninfected Caucasians, in HIV-1-infected individuals CCL3L1 copy number did not correlate either with long-term nonprogression or with CD4 cell count or viral load in chronic progressors. These findings underscore the heterogeneity of factors involved with long-term nonprogression when comparing cohorts of varying ethnic backgrounds.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.316
Teacher spread0.307 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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