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Record W2081557742 · doi:10.1093/cid/cis855

Editorial Commentary: Understanding Risk and Enhancing Safety in Immunotherapy Trials

2012· editorial· en· W2081557742 on OpenAlexaff
Deepali Kumar, Atul Humar

Bibliographic record

VenueClinical Infectious Diseases · 2012
Typeeditorial
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineImmunotherapyClinical trialMEDLINEIntensive care medicineImmunologyInternal medicineImmune system

Abstract

fetched live from OpenAlex

these patients for herpesvirus infections has provided novel insights into the effects of common viral infections with the use of potent exogenous immunosuppression in the nontransplant setting. In the current study, the authors performed careful laboratory and clinical monitoring for Epstein-Barr virus (EBV), cytomegalovirus (CMV), herpes simplex virus (HSV), and varicella zoster virus (VZV). Both primary infections and reactivations were observed but significantly increased morbidity was not seen in patients receiving either one or both immunosuppressive drugs compared with placebo. There was a trend toward greater EBV viral burden (P= .06) in patients receiving both daclizumab and MMF compared to those on no immunosuppression. However, most patients were either asymptomatic or had a self-limited mononucleosis-like syndrome. EBV is an important herpesvirus in transplantation; pediatric transplant patients especially are at greater risk of posttransplant lymphoproliferative disease (PTLD), primarily because they are often EBV-seronegative prior to transplantation and may receive a seropositive organ. In this study, EBV viral loads were reportedly not significantly different from viral loads measured in transplant patients at the same lab. In the transplant setting, high EBV viral loads have been correlated with an increased risk of PTLD [3]; however, other

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)0.004
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.003
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 head0.047
GPT teacher head0.396
Teacher spread0.349 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2012
Admission routes1
Has abstractyes

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