Supplementary Material for: Assessment of Suitability of Thrombolysis in Middle Cerebral Artery Infarction: A Proof of Concept Study of a Stereologically-Based Technique
Bibliographic record
Abstract
Background: The extent of cerebral ischemia, assessed by the Alberta Stroke Program Early CT Score (ASPECTS) method and unaided visual determination of the CT Summit Criterion, correlates with increased risk of intracerebral hemorrhage following rt-PA administration. Concerns about the accuracy of the unaided visual assessment in the estimation of infarct size and the conservative nature of the ASPECTS method led us to develop a new method (MCAGrid) based on stereological grid counting and a digital atlas of the middle cerebral artery (MCA) infarct territory. Methods: We tested the hypotheses that the stereological method increases the accuracy of infarct estimation and that the number of patients deemed eligible for thrombolysis is greater with this method than with existing methods. Four experienced radiologists with extensive neuroradiological experience examined the CT images of 19 patients with MCA territory stroke and determined patient eligibility for thrombolysis by: unaided visual determination of the CT Summit Criterion, MCAGrid, and the ASPECTS score. The χ2 test was used to compare the differences in the number of patients deemed ‘eligible’ for thrombolysis by the 3 imaging methods. Further, the unaided visual assessment and MCAGrid were compared with volumes calculated following manual segmentation of infarct, and the sensitivity, specificity and positive and negative likelihood ratios for these techniques were calculated. Results: In general, MCAGrid was better than unaided visual assessment in the prediction of >1/3 involvement of the MCA territory by infarct. The number of patients considered as ‘eligible’ for thrombolysis based on imaging criteria was significantly lower when ASPECTS criteria (15/76) were used than when unaided visual determination of the CT Summit Criterion (32/76; p < 0.01) or MCAGrid (59/76; p < 0.001) criteria were used. Conclusion: The choice of methods for rating infarct extent affects the number of patients ‘eligible’ for thrombolysis significantly. Furthermore, MCAGrid increased the accuracy with which infarct extent was estimated. These results provide justification for a prospective study of this technique in the setting of acute stroke.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.441 | 0.090 |
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".