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Secondary analysis of APPLE study suggests atorvastatin may reduce atherosclerosis progression in pubertal lupus patients with higher C reactive protein

2013· article· en· W2107358534 on OpenAlexafffund
Stacy P. Ardoin, Laura E. Schanberg, Christy Sandborg, Huiman X. Barnhart, Greg Evans, Eric Yow, Kelly L. Mieszkalski, Norman T. Ilowite, A. Eberhard, Lisa F. Imundo, Yuki Kimura, Deborah M. Levy, Emily von Scheven, Earl D. Silverman, Suzanne L. Bowyer, Lynn Punaro, Nora G. Singer, David D. Sherry, Deborah McCurdy, Marissa Klein‐Gitelman, Carol A. Wallace, Richard M. Silver, Linda Wagner‐Weiner, Gloria C. Higgins, Hermine I. Brunner, Lawrence Jung, Jennifer B. Soep, Ann M. Reed, Susan D. Thompson

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2013
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersNational Center for Advancing Translational SciencesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesSchool of Medicine, Indiana UniversitySchool of Medicine, Stanford UniversityPfizerHospital for Sick ChildrenNational Center for Research ResourcesGeorgia Clinical and Translational Science AllianceSeattle Children's Research InstituteUniversity of California, Los AngelesUniversity of South CarolinaCreighton UniversityCincinnati Children's Hospital Medical CenterNational Institutes of HealthCenter for Advanced Holocaust Studies, United States Holocaust Memorial MuseumBunning Food Allergy Institute, Ann and Robert H. Lurie Children's Hospital of ChicagoNationwide Children's HospitalChildren's Hospital of Philadelphia
KeywordsAtorvastatinMedicineInternal medicineC-reactive proteinPost-hoc analysisIntima-media thicknessPlaceboClinical endpointGastroenterologySubgroup analysisLipoproteinSystemic lupus erythematosusLipid profileRandomized controlled trialEndocrinologyCholesterolInflammationDiseasePathologyCarotid arteries

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 score0.004
Threshold uncertainty score0.526

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 head0.030
GPT teacher head0.324
Teacher spread0.294 · 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

Citations79
Published2013
Admission routes2
Has abstractno

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