Mechanical thrombectomy performs similarly in real world practice: a 2016 nationwide study from the Czech Republic
Bibliographic record
Abstract
BACKGROUND: Randomized clinical trials have proven mechanical thrombectomy (MT) to be a highly effective and safe treatment in acute stroke. The purpose of this study was to compare neurothrombectomy data from the Czech Republic (CR) with data from the HERMES meta-analysis. METHODS: Available nationwide data for the CR from 2016 from the Safe Implementation of Treatments in Stroke-Thrombectomy (SITS-TBY) registry for patients with terminal internal carotid artery (ICA) and/or middle cerebral artery (MCA) occlusions were compared with data from HERMES. CR and HERMES patients were comparable in age, sex, and baseline National Institutes of Health Stroke Scale scores. RESULTS: From a total of 1053 MTs performed in the CR, 845 (80%) were reported in the SITS-TBY. From these, 604 (72%) were included in this study. Occlusion locations were as follows (CR vs HERMES): ICA 22% versus 21% (P=0.16), M1 MCA 62% versus 69% (P=0.004), and M2 MCA 16% versus 8% (P<0.0001). Intravenous thrombolysis was given to 76% versus 83% of patients, respectively (P=0.003). Median onset to reperfusion times were comparable: 232 versus 285 min, respectively (P=0.66). A modified Thrombolysis in Cerebral Infarction score of 2b/3 was achieved in 74% (433/584) versus 71% (390/549) of patients, respectively (OR 1.17, 95% CI 0.90-1.5, P=0.24). There was no statistically significant difference in the percentage of parenchymalhematoma type 2 (OR 1.12, 95% CI 0.66-1.90, P=0.68). A modified Rankin Scale score of 0-2 at 3 months was achieved in 48% (184/268) versus 46% (291/633) of patients, respectively (OR 0.92, 95% CI 0.71-1.18, P=0.48). CONCLUSIONS: Data on efficacy, safety, and logistics of MT from the CR were similar to data from the HERMES collaboration.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".