How Much of the Outcome Improvement after Successful Recanalization is Explained by Follow-up Infarct Volume Reduction?
Bibliographic record
Abstract
ABSTRACT Background Follow-up infarct volume (FIV) is used as surrogate for treatment efficiency in Mechanical Thrombectomy (MT). In contrast to these assumptions, previous works suggest that MT-related infarct volume reduction has only limited association with outcome comparing MT vs. medical care. It remains unclear to what extent the causal relationship between successful recanalization vs. persistent occlusion and functional outcome is explained by treatment-related reduction in FIV. Results might allow quantification of pathophysiological effects and could improve the understanding of the value of FIV as imaging endpoint in clinical trials. Methods All patients from our institution enrolled in the German Stroke Registry from 05/2015-12/2019 with anterior circulation stroke, availability of the relevant clinical data and follow-up CT were analyzed. A mediation analysis was conducted to investigate the effect of successful recanalization (Tici≥2b) on good functional outcome (90d mRS≤2) with mediation through final infarct volume. Results 429 patients were included. 309(72 %) patients had a successful recanalization and 127(39%) achieved good functional outcome. Probability of good outcome was significantly associated with age (OR=0.89,p<0.001), pre-stroke mRS (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 significantly associated with ASPECTS (Coefficient(Co)=-26.13,p<0.001), NIHSS admission (Co=3.69,p<0.001), age (Co=-1.18,p<0.05) and successful recanalization (Co=-85.22,p<0.001). Mediation analysis suggest a 23 percentage points (pp) increase of probability of good functional outcome (95%CI:16pp-29pp) in patients with successful recanalization. 56% (95%CI:38%-78%) of the improvement in good outcome was explained FIV reduction. Conclusions 56% of the improvement of functional outcome after successful recanalization is explained by FIV reduction. Results corroborate established pathophysiological assumptions and confirm the value of infarct volume as imaging endpoint in clinical trials. 44% of the improvement in outcome is not explained by FIV reduction and reflects the remaining mismatch between radiological and clinical outcome measures. Trial Registration https://clinicaltrials.gov/ct2/show/NCT03356392 ( NCT03356392 )
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".