Heart Failure Is Not Associated with a Poor Outcome after Mechanical Thrombectomy in Large Vessel Occlusion of Cerebral Arteries
Bibliographic record
Abstract
The impact of heart failure on outcome in stroke patients is not fully understood. There is evidence for an increased mortality and morbidity, but it remains uncertain whether thrombectomy in patients with large vessel occlusion (LVO) in the anterior circulation is less effective in patients with heart failure compared to patients without. Retrospectively, we analyzed echocardiographic data of all patients in our stroke database, who underwent mechanical thrombectomy (n=668) for the presence of heart failure. Furthermore, we collected baseline characteristics and neurological and neuroradiological parameters. In the analysis, 373 of the 668 patients of our stroke database underwent echocardiography. Of these 373 patients, 90 patients (24%) suffered from heart failure with reduced left ventricular ejection fraction measured by echocardiography according to the current guidelines. After adjustment for age, the Alberta stroke program early CT score (ASPECTS), and time from symptom onset to recanalization, the analysis revealed that thrombectomy in patients with heart failure and LVO is not associated with less favorable outcome measured by the modified Rankin Scale after 90 days (3 (0-6) vs. 3 (1-5); p=0.380). Moreover, we could not find a significant difference in mortality compared to patients without heart failure (11.0% vs. 7.4%; p=0.313).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".