A research roadmap of future endovascular stroke trials
Bibliographic record
Abstract
The recent completion of the MR CLEAN trial1 and news of early stoppage of other stroke trials demonstrates the ability for the neurointerventional community to address a crucial question that has hindered the ability of intra-arterial therapy (IAT) to be offered more widely. The focus of future studies will now shift towards improving clinical outcomes in patients undergoing IAT. ### Imaging There is currently no consensus regarding the optimal imaging strategy for the selection of patients for intervention. The modality must be efficient, accurate, available and repeatable. Non-contrast CT using Alberta Stroke Program Early CT Score (ASPECTS) scoring,2 CT perfusion and MRI are all in widespread clinical usage at interventional stroke centers. A trial comparing different modes of imaging based patient selection would be valuable and currently does not exist. There are advantages and disadvantages to each technique with strong beliefs that each modality has its advantages. The question is whether the widespread availability, ease of access and time savings justify using non-contrast CT (supplemented by ASPECTS) as ‘good enough’ to select patients when compared to advanced imaging modalities that may be more specific to detecting ischemia. Developing an educational pathway with ASPECTS scoring to reduce inter-rater variability along with a standardized CT perfusion algorithm that can be replicated across institutions can allow for a trial examining this question to occur. The current landscape would potentially also allow for an MRI comparative trial. One starting point might be a core-lab adjudicated, prospective registry comparing pre- and post-treatment ASPECTS, computed tomography perfusion (CTP) and/or MRI data from …
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.192 | 0.251 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.057 | 0.020 |
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 source (direct Gemma or distilled Codex), 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".