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Standardized Definitions for Cardiogenic Shock Research and Mechanical Circulatory Support Devices: Scientific Expert Panel From the Shock Academic Research Consortium (SHARC)

2023· review· en· W4387249840 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCirculation · 2023
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of TorontoToronto General HospitalMcGill University Health CentreUniversity of Alberta HospitalUniversity of Alberta
FundersNational Heart, Lung, and Blood InstituteAgency for Healthcare Research and QualityCenters for Disease Control and PreventionARCA BiopharmaAbiomedUniversity of WashingtonBrigham and Women's HospitalAmgenAbbott VascularAbbott LaboratoriesZOLL Medical CorporationAstraZenecaNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsCardiogenic shockMedicineClinical trialGovernment (linguistics)StakeholderMEDLINERandomized controlled trialIntensive care medicineMedical educationMedical physicsSurgeryPublic relationsPathologyMyocardial infarctionCardiology

Abstract

fetched live from OpenAlex

The Shock Academic Research Consortium is a multi-stakeholder group, including representatives from the US Food and Drug Administration and other government agencies, industry, and payers, convened to develop pragmatic consensus definitions useful for the evaluation of clinical trials enrolling patients with cardiogenic shock, including trials evaluating mechanical circulatory support devices. Several in-person and virtual meetings were convened between 2020 and 2022 to discuss the need for developing the standardized definitions required for evaluation of mechanical circulatory support devices in clinical trials for cardiogenic shock patients. The expert panel identified key concepts and topics by performing literature reviews, including previous clinical trials, while recognizing current challenges and the need to advance evidence-based practice and statistical analysis to support future clinical trials. For each category, a lead (primary) author was assigned to perform a literature search and draft a proposed definition, which was presented to the subgroup. These definitions were further modified after feedback from the expert panel meetings until a consensus was reached. This manuscript summarizes the expert panel recommendations focused on outcome definitions, including efficacy and safety.

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.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.509
GPT teacher head0.438
Teacher spread0.072 · 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