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Record W2903244880 · doi:10.1371/journal.pone.0208142

Central adjudication of serious adverse events did not affect trial’s safety results: Data from the Efficacy of Nitric Oxide in Stroke (ENOS) trial

2018· article· en· W2903244880 on OpenAlexaff
Peter J. Godolphin, Alan Montgomery, Lisa J Woodhouse, Dániel Bereczki, Eivind Berge, Rónán Collins, Exuperio Díez‐Tejedor, John Gommans, Kennedy R. Lees, Şerefnur Öztürk, Stephen Phillips, Stuart Pocock, Kameshwar Prasad, Szabolcs Szatmári, Yongjun Wang, Philip M. Bath, Nikola Sprigg

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsDalhousie University
FundersResearch Trainees Coordinating CentreMedical Research CouncilStroke AssociationUniversity of GlasgowBupa FoundationNational Institute for Health and Care ResearchLondon School of Hygiene and Tropical Medicine
KeywordsAdjudicationMedicineClinical trialAdverse effectStroke (engine)Randomized controlled trialWaiverInternal medicineLaw

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Central adjudication of serious adverse events (SAEs) can be undertaken in clinical trials, especially for open-label studies where outcome assessment may be at risk of bias. This study explored the effect of central adjudication of SAEs on the safety results of the Efficacy of Nitric Oxide in Stroke (ENOS) Trial. METHODS: ENOS assigned patients with acute stroke at random to receive either transdermal glyceryl trinitrate (GTN) or no GTN and to Stop or Continue previous antihypertensive treatment. SAEs were reported by local investigators who were not blinded to treatment allocation. Central adjudicators, blinded to treatment allocation, reviewed the investigators reports and used evidence available to confirm or re-categorise the classification of event, likely causality, diagnosis and expectedness of event. RESULTS: Of 4011 patients enrolled in ENOS, 1473 SAEs were reported by local investigators; this was reduced to 1444 after the review by adjudicators, with 29 re-classified as not an SAE. There was fair agreement between investigators and adjudicators regarding likely causality, with 808 agreements and 644 disagreements (56% crude agreement, weighted kappa, κ = 0.31). Agreement increased upon dichotomisation of the causality categories, with 1432 agreements and 20 disagreements (99% crude agreement, kappa = 0.54). Repeating the main trial safety analysis with investigator reported events showed that adjudication had no effect on the main trial safety conclusions. CONCLUSIONS: In a large trial, with many SAEs reported, central adjudication of these events did not affect trial conclusions. This suggests that adjudication of SAEs in a clinical trial where the intervention already has a well-established safety profile may not be necessary. Potential efficiency savings (financial, logistical) can be made through not adjudicating SAEs.

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)0.249
metaresearch head score (Gemma)0.405
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2490.405
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.663
GPT teacher head0.462
Teacher spread0.201 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations8
Published2018
Admission routes1
Has abstractyes

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