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Comparison of Investigator-Reported and Clinical Event Committee–Adjudicated Outcome Events in GLASSY

2021· article· en· W3128614766 on OpenAlexaff
Sergio Leonardi, Mattia Branca, Anna Franzone, Eugène McFadden, Raffaele Piccolo, Peter Jüni, Pascal Vranckx, Philippe Gabríel Steg, Patrick W. Serruys, Edouard Benit, Christoph Liebetrau, Luc Janssens, Maurizio Ferrario, Aleksander Żurakowski, Roberto Diletti, Marcello Dominici, Kurt Huber, Ton Slagboom, Paweł Buszman, Leonardo Bolognese, Carlo Tumscitz, Krzysztof Bryniarski, Adel Aminian, Mathias Vrolix, Ivo Petrov, Scot Garg, Janusz Prokopczuk, Christian W. Hamm, Dik Heg, Stephan Windecker, Marco Valgimigli

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsAdjudicationConcordanceMedicineInternal medicineStroke (engine)

Abstract

fetched live from OpenAlex

BACKGROUND: Event adjudication by a clinical event committee (CEC) provides a standardized, independent outcome assessment. However, the added value of CEC to investigators reporting remains debated. GLASSY (GLOBAL LEADERS Adjudication Sub-Study) implemented, in a subset of the open-label, investigator-reported (IR) GLOBAL LEADERS trial, an independent adjudication process of reported and unreported potential outcome events (triggers). We describe metrics of GLASSY feasibility and efficiency, diagnostic accuracy of IR events, and their concordance with corresponding CEC-adjudicated events. METHODS: We report the proportion of myocardial infarction, bleeding, stroke, and stent thrombosis triggers with sufficient evidence for assessment (feasibility) that were adjudicated as outcome events (efficiency), stratified by source (IR or non-IR). Using CEC-adjudicated events as criterion standard, we describe sensitivity, specificity, positive and negative predictive value, and global diagnostic accuracy of IR events. Using Gwet AC coefficient, we examine the concordance between IR- and corresponding CEC-adjudicated triggers. There was sufficient evidence for assessment for 2592 (98.3%) of 2636 triggers. RESULTS: Overall, the adjudicated end point-to-trigger ratio was high and similar between IR- (88%) and non-IR-reported (87%) triggers. The global diagnostic accuracy and concordance between IR-reported and CEC-adjudicated outcome events was 0.70 (95% CI, 0.65-0.74) and 0.54 (95% CI, 0.45-0.62), respectively, for myocardial infarction; 0.77 (95% CI, 0.75-0.79) and 0.71 (95% CI, 0.68-0.74) for bleeding; 0.70 (95% CI, 0.62-0.79) and 0.59 (95% CI, 0.43-0.74) for stroke; 0.59 (95% CI, 0.52-0.66) and 0.39 (95% CI, 0.25-0.53) for stent thrombosis. For IR bleedings, the concordance with the CEC on type of events was generally weak. CONCLUSIONS: Implementing CEC adjudication in a pragmatic open-label trial with IR events is feasible and efficient. Our findings of modest global diagnostic accuracy for IR events and generally weak concordance between investigators and CEC support the role for CEC adjudication in such settings. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03231059.

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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)0.121
metaresearch head score (Gemma)0.074
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1210.074
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.828
GPT teacher head0.606
Teacher spread0.222 · 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 designObservational
Domainnot available
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

Citations11
Published2021
Admission routes1
Has abstractyes

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