Progression of cerebral infarction before and after thrombectomy is modified by prehospital pathways
Bibliographic record
Abstract
Background Evidence of the consequences of different prehospital pathways before mechanical thrombectomy (MT) in large vessel occlusion stroke is inconclusive. The aim of this study was to investigate the infarct extent and progression before and after MT in directly admitted (mothership) versus transferred (drip and ship) patients using the Alberta Stroke Program Early CT Score (ASPECTS). Methods ASPECTS of 535 consecutive large vessel occlusion stroke patients eligible for MT between 2015 to 2019 were retrospectively analyzed for differences in the extent of baseline, post-referral, and post-recanalization infarction between the mothership and drip and ship pathways. Time intervals and transport distances of both pathways were analyzed. Multiple linear regression was used to examine the association between infarct progression (baseline to post-recanalization ASPECTS decline), patient characteristics, and logistic key figures. Results ASPECTS declined during transfer (9 (8–10) vs 7 (6-9), p<0.0001), resulting in lower ASPECTS at stroke center presentation (mothership 9 (7–10) vs drip and ship 7 (6–9), p<0.0001) and on follow-up imaging (mothership 7 (4–8) vs drip and ship 6 (3–7), p=0.001) compared with mothership patients. Infarct progression was significantly higher in transferred patients (points lost, mothership 2 (0–3) vs drip and ship 3 (2–6), p<0.0001). After multivariable adjustment, only interfacility transfer, preinterventional clinical stroke severity, the degree of angiographic recanalization, and the duration of the thrombectomy procedure remained predictors of infarct progression (R 2=0.209, p<0.0001). Conclusions Infarct progression and postinterventional infarct extent, as assessed by ASPECTS, varied between the drip and ship and mothership pathway, leading to more pronounced infarction in transferred patients. ASPECTS may serve as a radiological measure to monitor the benefit or harm of different prehospital pathways for MT.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".