Direct mechanical thrombectomy in tPA-ineligible and -eligible patients versus the bridging approach: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Whether pretreatment with intravenous thrombolysis prior to mechanical thrombectomy (IVT+MTE) adds additional benefit over direct mechanical thrombectomy (dMTE) in patients with large vessel occlusions (LVO) is a matter of debate. METHODS: This study-level meta-analysis was presented in accord with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Pooled effect sizes were calculated using the inverse variance heterogeneity model and displayed as summary Odds Ratio (sOR) and corresponding 95% confidence interval (95% CI). Sensitivity analysis was performed by distinguishing between studies including dMTE patients eligible for IVT (IVT-E) or ineligible for IVT (IVT-IN). Primary outcome measures were functional independence (modified Rankin Scale≤2) and mortality at day 90, successful reperfusion, and symptomatic intracerebral hemorrhage. RESULTS: Twenty studies, incorporating 5279 patients, were included. There was no evidence that rates of successful reperfusion differed in dMTE and IVT+MTE patients (sOR 0.93, 95% CI 0.68 to 1.28). In studies including IVT-IN dMTE patients, patients undergoing dMTE tended to have lower rates of functional independence and had higher odds for a fatal outcome as compared with IVT+MTE patients (sOR 0.78, 95% CI 0.61 to 1.01 and sOR 1.45, 95% CI 1.22 to 1.73). However, no such treatment group effect was found when analyses were confined to cohorts with a lower risk of selection bias (including IVT-E dMTE patients). CONCLUSION: The quality of evidence regarding the relative merits of IVT+MTE versus dMTE is low. When considering studies with lower selection bias, the data suggest that dMTE may offer comparable safety and efficacy as compared with IVT+MTE. The conduct of randomized-controlled clinical trials seems justified.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.066 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".