Use of Thick Maximum‐Intensity Projection Brain Computed Tomography Angiography for Evaluation of Baseline Collateral Status Improves Interrater Agreement
Bibliographic record
Abstract
Background In acute ischemic stroke caused by large‐vessel occlusion, tissue viability is dependent on the blood supply from leptomeningeal collaterals until reperfusion is achieved. Rapid and accurate evaluation of baseline collateral status is a key marker of eligibility for endovascular therapy but can be challenging to interpret using source images of the computed tomography angiography (SI‐CTA). Our objective was to assess whether the use of thick maximum‐intensity projection computed tomography angiography (MIP‐CTA) improves interrater agreement for evaluation of baseline collaterals status between stroke trainees and an expert stroke neurologist. Methods An expert stroke neurologist and 2 stroke trainees independently reviewed images from 40 brain CTA scans with anterior circulation large‐vessel occlusion and assessed collateral status using the Tan collateral scoring system using SI‐CTA in the first reading and then using MIP‐CTA in the second reading. We calculated interrater agreement and recorded the total time needed in each reading. Results Interrater agreement was fair between the 2 stroke fellows and stroke expert when using SI‐CTA (κ=0.45 with 52.5% agreement). After using MIP‐CTA, interrater agreement improved to moderate (κ=0.69 with 70% agreement). The median reading time was 1.89 minutes per scan using SI‐CTA and 1.00 minute per scan using MIP‐CTA ( P <0.0001). Conclusions We show that using MIP‐CTA, when compared with SI‐CTA, shortens interpretation time and improves interrater agreement between stroke trainees and a stroke imaging expert for the evaluation of baseline collaterals in patients presenting with anterior circulation large‐vessel occlusion.
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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 | 0.052 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".