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Record W4214684786 · doi:10.3410/f.732604709.793576500

Faculty Opinions recommendation of Randomized, controlled trial of TNF-α antagonist in CTL-mediated severe cutaneous adverse reactions.

2020· dataset· en· W4214684786 on OpenAlexaff
Michele Ramien

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2020
Typedataset
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsUniversity of Calgary
FundersNational Science CouncilChang Gung Memorial HospitalClinical Trial Center, China Medical University Hospital
KeywordsEtanerceptMedicineAdverse effectGranulysinScarsPopulationCTL*Tumor necrosis factor alphaToxic epidermal necrolysisImmunologyDermatologyGastroenterologyPharmacologyInternal medicineSurgeryImmune system

Abstract

fetched live from OpenAlex

BACKGROUND. Cytotoxic T lymphocyte-mediated (CTL-mediated) severe cutaneous adverse reactions (SCARs), including Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), are rare but life-threatening adverse reactions commonly induced by drugs. Although high levels of CTL-associated cytokines, chemokines, or cytotoxic proteins, including TNF- and granulysin, were observed in SJS-TEN patients in recent studies, the optimal treatment for these diseases remains controversial. We aimed to evaluate the efficacy, safety, and therapeutic mechanism of a TNF- antagonist in CTL-mediated SCARs.

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)0.003
metaresearch head score (Gemma)0.051
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.241
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.025
GPT teacher head0.342
Teacher spread0.317 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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