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Record W2975456609 · doi:10.1161/strokeaha.119.026238

Observer Agreement on Computed Tomography Perfusion Imaging in Acute Ischemic Stroke

2019· article· en· W2975456609 on OpenAlexaboutno aff
Salwa El Tawil, Grant Mair, Xuya Huang, Eleni Sakka, Jeb Palmer, Ian Ford, Lalit Kalra, Joanna M. Wardlaw, Keith W. Muir, Alessandro Adami, Alfonso Cerase, Ana García, Anders von Heijne, André Peeters, Andrea Zini, Ângelo Carneiro, Chris Patterson, Christine Roffe, Daniel Z. Freedman, Daniel Scoffings, Derk Krieger, Dipayan Mitra, Eivind Berge, Elena Adela Cora, Eoin O’Brien, Eric Bertholds, Ethem Murat, Fiona Moreton, Garryck Tan, Gillian Potter, Giuseppe Rinaldi, Jeremy Madigan, Joe Leyon, Johann Du Plessis, Jonathan Hewitt, J. Egido, L. Sztriha, Magnus Esbjoernsson, Manuel Correia, Martin Griebe, Michelle Dharmasiri, Olga Kirmi, Olivia Geraghty, Pablo García-Bermejo, Patrick Sutton, Pervinder Bhogal, Phil White, Phillip Ferdinand, Qazi T Anjum, Robin Sellar, Rüdiger von Kummer, Sreeman Andole, Sriram Vundavalli, Tom Webb, Tilak Das, Tomasz Matys, Tony Goddard, Vamsi Gontu, Vijay Sawlani, Volker Puetz, William Whiteley

Bibliographic record

VenueStroke · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicinePenumbraThrombolysisPerfusion scanningStroke (engine)RadiologyPerfusionTenecteplaseCerebral blood flowNeuroradiologyNuclear medicineAcute strokeBlood volumeIschemiaNeurologyInternal medicineTissue plasminogen activator

Abstract

fetched live from OpenAlex

Background and Purpose- Computed tomography (CT) perfusion (CTP) provides potentially valuable information to guide treatment decisions in acute stroke. Assessment of interobserver reliability of CTP has, however, been limited to small, mostly single center studies. We performed a large, internet-based study to assess observer reliability of CTP interpretation in acute stroke. Methods- We selected 24 cases from the IST-3 (Third International Stroke Trial), ATTEST (Alteplase Versus Tenecteplase for Thrombolysis After Ischaemic Stroke), and POSH (Post Stroke Hyperglycaemia) studies to illustrate various perfusion abnormalities. For each case, observers were presented with noncontrast CT, maps of cerebral blood volume, cerebral blood flow, mean transit time, delay time, and thresholded penumbra maps (dichotomized into penumbra and core), together with a short clinical vignette. Observers used a structured questionnaire to record presence of perfusion deficit, its extent compared with ischemic changes on noncontrast CT, and an Alberta Stroke Program Early CT Score for noncontrast CT and CTP. All images were viewed, and responses were collected online. We assessed observer agreement with Krippendorff-α. Intraobserver agreement was assessed by inviting observers who reviewed all scans for a repeat review of 6 scans. Results- Fifty seven observers contributed to the study, with 27 observers reviewing all 24 scans and 17 observers contributing repeat readings. Interobserver agreement was good to excellent for all CTP. Agreement was higher for perfusion maps compared with noncontrast CT and was higher for mean transit time, delay time, and penumbra map (Krippendorff-α =0.77, 0.79, and 0.81, respectively) compared with cerebral blood volume and cerebral blood flow (Krippendorff-α =0.69 and 0.62, respectively). Intraobserver agreement was fair to substantial in the majority of readers (Krippendorff-α ranged from 0.29 to 0.80). Conclusions- There are high levels of interobserver and intraobserver agreement for the interpretation of CTP in acute stroke, particularly of mean transit time, delay time, and penumbra maps.

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)0.030
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.087
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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 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

Citations10
Published2019
Admission routes1
Has abstractyes

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