P142/66 A DELPHI consensus statement on the definition of a tough clot
Bibliographic record
Abstract
<h3>Introduction</h3> Research into challenging clots in mechanical thrombectomy has substantially increased in recent years, particularly imaging, intra-procedural, and clot properties. However, integrating them to identify a challenging occlusion is not well established. <h3>Aim of Study</h3> Explore the opinions of neuro-interventional experts and clot researchers to define these tough clots. <h3>Methods</h3> A modified DELPHI technique was used before and live during CLOT SUMMIT 7.0. Panelists answered three iterative question rounds in which they indicated their certainty level from 1 (very uncertain) to 4 (very certain) 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. Consensus was defined as greater than 50% agreement. Certainty levels of 3.0 or greater were regarded as high certainty. <h3>Results</h3> After a total of 3 DELPHI rounds, consensus was reached on 16 of 30 questions, where 8 were of high certainty (27%). They were combined to produce a holistic definition of a challenging clot: A clot that could be white colored or calcified, that is stiff, hard, sticky or adherent, with possible calcification visible on imaging, and during thrombectomy is difficult to pass and resistant to pulling. <h3>Conclusion</h3> A live DELPHI consensus from experts in thrombectomy and clot research suggest the features of a challenging/tough clot, which may narrow focus for future development of specialized tools for a priori identification of tough clots. <h3>Disclosure of Interest</h3> MM and PB are employees of Cerenovus
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".