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Record W3214627594 · doi:10.1161/svin.121.000177

Standardized Reporting of Workflow Metrics in Acute Ischemic Stroke Treatment: Why and How?

2021· article· en· W3214627594 on OpenAlexaff
Mayank Goyal, Jeffrey L. Saver, Aravind Ganesh, Rosalie McDonough, Yvo B.W.E.M. Roos, Grégoire Boulouis, Martin Kurz, Marios‐Nikos Psychogios, Staffan Holmin, Charles B.L.M. Majoie, Romain Bourcier, Ronil V. Chandra, Shinichi Yoshimura, Dileep R. Yavagal, Benjamin Gory, Christian Taschner, Brian Buck, Ashutosh P. Jadhav, Michael D. Hill, Johanna M. Ospel

Bibliographic record

VenueStroke Vascular and Interventional Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsWorkflowInterquartile rangeMedicineConsistency (knowledge bases)Stroke (engine)PercentileComputer scienceMedical physicsInternal medicineDatabaseArtificial intelligenceStatisticsEngineering

Abstract

fetched live from OpenAlex

The benefit of acute ischemic stroke (AIS) treatment is highly time dependent. Although studies on workflow improvement in AIS are increasingly gaining attention, there is a lack of consensus and consistency regarding the definition, measurement, and reporting of AIS workflow times. We discuss the challenges related to defining and measuring workflow times in AIS and propose a basic set of time intervals that should be reported in AIS workflow studies. We particularly focus on patients undergoing mechanical thrombectomy. Importantly, endovascular treatment workflow times should always be reported in conjunction with reperfusion quality because one is not informative without the other. We further suggest standardized reporting of workflow times that includes the 90th percentile in addition to medians and interquartile ranges, means, and SDs. The proposed methodology serves as a framework for AIS studies and aids further discussion on workflow-related AIS research.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2021
Admission routes1
Has abstractyes

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