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Record W4385398779 · doi:10.1136/jnis-2023-snis.236

E-136 What is a challenging clot? A delphi consensus statement from the clot summit group

2023· article· en· W4385398779 on OpenAlexaff
Mahmood Mirza, Johanna M. Ospel, Patrick A. Brouwer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCertaintyMedical physicsMedicineDelphi methodOcclusionComputer scienceArtificial intelligenceSurgeryMathematics

Abstract

fetched live from OpenAlex

Introduction Research into occlusion factors has substantially increased in recent years, including imaging, flow patterns, clot composition, histology, immunohistochemistry, and biomechanical properties. However, integrating them into clinical practice to identify a challenging occlusion prior to clot retrieval is not well established. Methods A modified DELPHI technique was used before and during CLOT SUMMIT 7.0, which included experts in thrombectomy and clot research from different specialties. Panelists answered three iterative question rounds, in which they indicated their certainty level on the association of 30 specific clot features as indicators for difficult-to-recanalize target occlusions. The features were grouped into 5 domains: histological, imaging, biomechanical, procedural, and clinical factors. The first round included open-ended questions and formed the basis for subsequent rounds, in which closed ended questions were used. Consensus was defined as ≥ 50% agreement among the panelists. Certainty was rated from 1 (‘very uncertain’) to 4 (‘very certain’) in the final round, and mean levels of 3.0 or greater were regarded as high certainty. Results A total of 3 DELPHI rounds were performed, the last two in a live setting. Consensus was reached on 16 out of 30 questions, of which 8 were of high certainty (23%). Except for the clinical factors domain, all others had at last at least one clot feature with consensus and high certainty. Of those, the biomechanical domain produced the most clot features with consensus and high certainty (75%) while the imaging domain produced the least (8.3%). The 8 clot features were combined to produce a holistic definition of a challenging clot: A white coloured or calcified clot that’s stiff, hard, sticky or adherent, that could be calcified on imaging, and during thrombectomy is difficult to pass and resistant to pulling. There was also consensus but with less certainty (2.6/4) that the endovascular (EVT) technique should be switched after the third unsuccessful attempt. Conclusions A live DELPHI consensus from experts in thrombectomy and clot research suggest the features of a challenging clot, which most aptly describe a tough clot: A white coloured or calcified clot that’s stiff, hard, sticky or adherent, that could be calcified on imaging, and during thrombectomy is difficult to pass and resistant to pulling. This may help clinicians and researchers focus on using and developing specialized tools for a priori identification of tough clots for swift recanalization. Disclosures M. Mirza: 5; C; Cerenovus. J. Ospel: None. P. Brouwer: 5; C; Cerenovus.

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)0.039
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.085
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.006
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0660.018

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 head0.030
GPT teacher head0.281
Teacher spread0.250 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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