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Record W2767469906 · doi:10.1002/cpt.890

Research Directions in Genetic Predispositions to Stevens–Johnson Syndrome / Toxic Epidermal Necrolysis

2017· review· en· W2767469906 on OpenAlexaff
Teri A. Manolio, Carolyn M. Hutter, Mark Avigan, Ricardo Cibotti, Robert L. Davis, Joshua C. Denny, Lois La Grenade, Lisa M. Wheatley, Mary Carrington, Wasun Chantratita, Wen‐Hung Chung, Andrea D. Dalton, Wen‐Hung Chung, Ming Ta Michael Lee, J. Steven Leeder, Juan J.L. Lertora, Surakameth Mahasirimongkol, Howard L. McLeod, Maja Mockenhaupt, Michael Pacanowski, Elizabeth J. Phillips, Simone Pinheiro, Munir Pirmohamed, Cynthia Sung, Wimon Suwankesawong, Lauren A. Trepanier, Santa J. Tumminia, David L. Veenstra, Rika Yuliwulandari, Neil H. Shear

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2017
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsUniversity of Toronto
FundersNational Human Genome Research InstituteNational Institutes of HealthNational Cancer InstituteNational Institute of General Medical SciencesMedical Research CouncilNational Institute for Health and Care Research
KeywordsToxic epidermal necrolysisDrug reactionMedicineIncidence (geometry)DrugDermatologyGenetic predispositionComplicationIntensive care medicineAlleleSurgeryPharmacologyInternal medicineGeneticsDiseaseBiology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.002

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 head0.481
GPT teacher head0.605
Teacher spread0.124 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations18
Published2017
Admission routes1
Has abstractyes

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