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Record W2979546650 · doi:10.1080/14740338.2019.1675634

A drug safety review of treating eosinophilic asthma with monoclonal antibodies

2019· review· en· W2979546650 on OpenAlexaffabout
Patrick Mitchell, Richard Leigh

Bibliographic record

VenueExpert Opinion on Drug Safety · 2019
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineExpert opinionAsthmaAdverse effectNiceClinical trialIntensive care medicineEuropean commissionEosinophilicEuropean unionImmunologyPharmacologyPathology

Abstract

fetched live from OpenAlex

Introduction: The last two decades have seen significant progress in the treatment of severe asthma especially the severe eosinophilic phenotype. This review article serves to update the reader on the known safety profiles of these medications. It does not serve as a review of their clinical efficacies.Areas covered: All four of the currently approved monoclonal antibodies (biologics) used in the treatment of severe asthma are discussed with reference to the known safety data garnered from clinical trials and real world evidence. A fifth, approved by The European Commission and FDA, but not yet by NICE or Health Canada, is also discussed.Expert opinion: For each of the five biologics the authors shall summarize the known safety profiles and also the potential adverse effects as their usage is extended long term with suggestions for real world studies to help us develop our knowledge base.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.036
GPT teacher head0.355
Teacher spread0.319 · 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 designSystematic review
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

Citations14
Published2019
Admission routes2
Has abstractyes

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