Sex differences in clot, vessel and tissue characteristics in patients with a large vessel occlusion treated with endovascular thrombectomy
Bibliographic record
Abstract
INTRODUCTION: To improve our understanding of the relatively poor outcome after endovascular treatment (EVT) in women we assessed possible sex differences in baseline neuroimaging characteristics of acute ischemic stroke patients with large anterior vessel occlusion (LVO). PATIENTS AND METHODS: We included all consecutive patients from the MR CLEAN Registry who underwent EVT between 2014 and 2017. On baseline non-contrast CT and CT angiography, we assessed clot location and clot burden score (CBS), vessel characteristics (presence of atherosclerosis, tortuosity, size, and collateral status), and tissue characteristics with the Alberta Stroke Program Early Computed Tomography Score (ASPECTS). Radiological outcome was assessed with the extended thrombolysis in cerebral infarction score (eTICI) and functional outcome with the modified Rankin Scale score (mRS) at 90 days. Sex-differences were assessed with multivariable regression analyses with adjustments for possible confounders. RESULTS: 3180 patients were included (median age 72 years, 48% women). Clots in women were less often located in the intracranial internal carotid artery (ICA) (25%vs 28%, odds ratio (OR) 0.85;95% confidence interval: 0.73-1.00). CBS was similar between sexes (median 6, IQR 4-8). Intracranial (aOR 0.73;95% CI:0.62-0.87) and extracranial (aOR 0.64;95% CI:0.43-0.95) atherosclerosis was less prevalent in women. Vessel tortuosity was more frequent in women in the cervical ICA (aOR 1.89;95% CI:1.39-2.57) and women more often had severe elongation of the aortic arch (aOR 1.38;95% CI:1.00-1.91). ICA radius was smaller in women (2.3vs 2.5 mm, mean difference 0.22;95% CI:0.09-0.35) while M1 radius was essentially equal (1.6vs 1.7 mm, mean difference 0.09;95% CI:-0.02-0.21). Women had better collateral status (⩾50% filling in 62%vs 53% in men, aOR 1.48;95% CI:1.29-1.70). Finally, ASPECT scores were equal between women and men (median 9 in both sexes, IQR 8-10vs 9-10). Reperfusion rates were similar between women and men (acOR 0.94;95% CI:0.83-1.07). However, women less often reached functional independence than men (34%vs 46%, aOR 0.68;95% CI:0.53-0.86). DISCUSSION AND CONCLUSION: On baseline imaging of this Dutch Registry, men and women with LVO mainly differ in vessel characteristics such as atherosclerotic burden, extracranial vessel tortuosity, and collateral status. These sex differences do not result in different reperfusion rates and are, therefore, not likely to explain the worse functional outcome in women after EVT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".