The probability of middle cerebral artery MRA flow signal abnormality with quantified CT ischaemic change: targets for future therapeutic studies
Bibliographic record
Abstract
OBJECTIVES: In this study we define the probability of vascular abnormality in the middle cerebral artery (MCA) territory according to the extent of ischaemic change seen using computed tomography (CT). We assessed the sensitivity and specificity of the hyperdense middle cerebral artery (HMCA) and the "dot" sign using magnetic resonance angiography (MRA). METHODS: Patients presenting with ischaemic stroke had a CT scan (<6 h) prior to MRI (<7 h). A quantitative CT scoring system (ASPECTS) was applied to CT and diffusion weighted images (DWI) at baseline and follow up (24 h) by five independent observers. The presence of HMCA and the MCA "dot" sign was also evaluated. An expert reader assessed the 3D time of flight (TOF) MRA in the anterior circulation for areas of decreased vascular signal in the MCA territory, with an absent signal taken to represent severely reduced or absent flow. RESULTS: A total of 100 consecutive patients had baseline CT and MR scans. The median NIHSS was 9. The median CT ASPECTS was 8 and equalled the median DWI ASPECTS. There were a total of 10 HMCA and 19 MCA "dot" signs, with four patients having both HMCA and "dot" signs. A total of 47 MRA flow signal abnormalities were observed in the anterior circulation. CONCLUSIONS: In the absence of accessible neurovascular imaging, the extent of CT ischaemia (ASPECTS) is a strong predictor of vascular occlusion. The CT hyperdense artery signs have a high positive predictive value but low negative predictive value.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".