National trends in patient characteristics, interventional techniques and outcomes of endovascular treatment for acute ischaemic stroke: Final results of the MR CLEAN Registry (2014–2018)
Bibliographic record
Abstract
INTRODUCTION: Endovascular thrombectomy (EVT) procedures and workflow have evolved over the years. We examined trends in patient characteristics, EVT techniques and outcomes over 5 years in the Netherlands. PATIENTS AND METHODS: Data from the MR CLEAN Registry (2014-2018) were analysed, including patients treated with EVT for anterior circulation acute ischaemic stroke (AIS). Patients were grouped by year of inclusion except for the linear regression analysis where the inclusion date was used. Baseline predicted probability of poor outcome (modified Rankin Scale (mRS) score 3-6) was calculated using a validated prediction model. Primary outcome was mRS score at 90 days. Secondary outcomes included workflow times, EVT techniques, successful reperfusion (eTICI ⩾ 2B) and symptomatic intracranial haemorrhage (sICH). Time trends were analysed using multivariable regression models (adjusted common odds ratios (acOR) per year). RESULTS: = 0.06). Over time, functional outcomes improved (acOR 1.14 per year, 95%CI: 1.09-1.20); mortality decreased (aOR 0.88 per year, 95%CI: 0.83-0.94). EVT under local anaesthesia increased (from 46% in 2014 to 70% in 2018; aOR 1.15, 95%CI: 1.10-1.22), as did use of direct aspiration (13%-36%; aOR 1.43, 95%CI: 1.35-1.53). Successful reperfusion became more frequent (aOR 1.32 per year, 95%CI: 1.25-1.40), despite needing more attempts (1 in 2014 vs 2 in 2018, aOR 0.93 per year, 95%CI: 0.89-0.98). Incidence of sICH remained unchanged (5% vs 5%, aOR 0.99 per year, 95%CI: 0.89-1.09). Time from emergency room to groin puncture reduced by 7 min per year (95%CI: 5-8). DISCUSSION AND CONCLUSION: Enhanced workflow and increased EVT experience may have led to shorter time to treatment and more frequent successful reperfusion, with better functional outcomes over 5 years, despite treating older, more dependent patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".