SELECTion criteria for large core trials: dogma or data?
Bibliographic record
Abstract
We thank the Editors of JNIS for alerting us in advance to the concerns about SELECT2 raised by Jadhav1 and colleagues and appreciate the opportunity to explain the rationale for the study design and clarify the benefits of including perfusion imaging-based selection criteria. We are confident that SELECT2 will provide high-level, reliable data regarding the safety and efficacy of endovascular thrombectomy (EVT) for large core patients. The choice of imaging modality for identifying large core in acute ischemic stroke remains an area of considerable debate. Magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI), computed tomography (CT) or MR perfusion imaging and Alberta Stroke Program Early CT Score (ASPECTS) have all been proposed and studied. At present, there is no clear consensus on which imaging modality is best for identifying patients with large core. It is important to recognize that the early window randomized EVT trials used a broad range of imaging selection criteria. The imaging selection criteria for the initial five pivotal trials ranged from allowing patients to be enrolled regardless of the degree of early infarct signs,2 to studies that required a specific ASPECTS score range in addition to other imaging criteria,3–5 to EXTEND IA6 where the ASPECTS score was not considered, and CT perfusion (CTP) mismatch with a maximum estimated core size was required. All five trials were successful but with substantial variability in the treatment effect, leaving uncertainty as to the optimal imaging approach as well as whether there are patient subgroups who do not benefit. In fact, those utilizing perfusion mismatch criteria (EXTEND-IA, SWIFT PRIME) had higher rates of modified Rankin Scale (mRS) scores 0–2 and larger treatment effects, compared with other trials.2–6 Even if a treatment has a clear benefit in most patients, there can be important subgroups that do not …
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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 | 0.455 | 0.758 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.024 | 0.045 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".