Back to the Future: How Much or Little Imaging Is Really Necessary After Endovascular Thrombectomy Large‐Core Trials
Bibliographic record
Abstract
stroke thrombectomy thrombolysis The significance of imaging for stroke diagnosis cannot be understated, and our patient selection for reperfusion therapies evolved dramatically over time from almost all-inclusive to superselection along with proliferation of multimodal computed tomography (CT), which included noncontrast CT (NCCT), CT angiography (CTA) and CT perfusion (CTP), magnetic resonance imaging and postprocessing softwares.Let us in turn examine how little or how much imaging was used during the pivotal trials.The NINDS-rt-PA (National Institute of Neurological Disorders and Stroke Recombinant Tissue-Type Plasminogen Activator) stroke study was conducted from 1991 to 1994, and at that time all that was available for hyperacute stroke assessment was an NCCT scan. 1 The only inclusion criterion on imaging was "no evidence of intracerebral hemorrhage," while early ischemic changes were not an exclusion before randomization.Hence, the Alberta Stroke Program Early CT Score (ASPECTS) 2 was developed after publication of the NINDS trial and was applied in subsequent trials to select patients with smaller ischemic cores who would likely benefit from reperfusion.As 1 of us teaches, careful ASPECTS analysis can predict what one will see
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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 | 0.122 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".