How much of the outcome improvement after successful recanalization is explained by follow-up infarct volume reduction?
Bibliographic record
Abstract
BACKGROUND: Follow-up infarct volume (FIV) is used as surrogate for treatment efficiency in mechanical thrombectomy (MT). However, previous works suggest that MT-related FIV reduction has only limited association with outcome comparing MT independently of recanalization success versus medical care. It remains unclear to what extent the relationship between successful recanalization versus persistent occlusion and functional outcome is explained by FIV reduction. OBJECTIVE: To determine whether FIV mediates the relationship between successful recanalization and functional outcome. METHODS: All patients from our institution enrolled in the German Stroke Registry (May 2015-December 2019) with anterior circulation stroke; availability of the relevant clinical data, and follow-up-CT were analyzed. The effect of FIV reduction on functional outcome (90-day modified Rankin Scale (mRS) score ≤2) after successful recanalization (Thrombolysis in Cerebral Infarction ≥2b) was quantified using mediation analysis. RESULTS: 429 patients were included, of whom, 309 (72 %) had successful recanalization and 127 (39%) had good functional outcome. Good outcome was associated with age (OR=0.89, P<0.001), pre-stroke mRS score (OR=0.38, P<0.001), FIV (OR=0.98, P<0.001), hypertension (OR=2.08, P<0.05), and successful recanalization (OR=3.57, P<0.01). Using linear regression in the mediator pathway, FIV was associated with Alberta Stroke program Early CT Score (coefficient (Co)=-26.13, P<0.001), admission National Institutes of Health Stroke Scale score (Co=3.69, P<0.001), age (Co=-1.18, P<0.05), and successful recanalization (Co=-85.22, P<0.001). Successful recanalization increased the probability of good outcome by 23 percentage points (pp) (95% CI 16pp to 29pp). 56% (95% CI 38% to 78%) of the improvement in good outcome was explained by FIV reduction. CONCLUSION: 56% (95% CI 38% to 78%) of outcome improvement after successful recanalization was explained by FIV reduction. Results corroborate pathophysiological assumptions and confirm the value of FIV as an imaging endpoint in clinical trials. 44% (95% CI 22% to 62%) of the improvement in outcome was not explained by FIV reduction and reflects the remaining mismatch between radiological and clinical outcome measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".