Addressing Heterogeneity in the Large-Core Trials: A Case for Standardized Imaging Analysis
Bibliographic record
Abstract
Infarct detection is critically dependent on the imaging modality that is used and the criteria for defining tissue infarction. The recent trials of large-core thrombectomy used heterogeneous imaging methods to identify patients with large ischemic cores. Moreover, the Alberta Stroke Program Early CT Score methodology was not harmonized between the trials. Consequently, the large-core trial populations were distinct. To pool the populations in a clinically meaningful way, data should be pooled by imaging modality and time window. The imaging should be re-adjudicated using standardized criteria for imaging analysis and Alberta Stroke Program Early CT Score grading. This standardized approach can be disseminated into clinical practice so that the pooled treatment effect estimates can guide real-world patient care.
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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 | 0.472 | 0.549 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.013 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".