Bridging thrombolysis with tenecteplase versus endovascular thrombectomy alone for large-vessel anterior circulation stroke: a target trial emulation analysis
Bibliographic record
Abstract
BACKGROUND: Whether bridging thrombolysis with tenecteplase is beneficial compared with thrombectomy alone in patients who had a stroke with large-vessel occlusion remains unclear. METHODS: This is a causal inference study of observational data from the trials SWIFT DIRECT and EXTEND-IA TNK Parts 1 and 2 applying target trial emulation. We compared patients receiving thrombectomy alone to patients receiving tenecteplase 0.25 mg/kg or 0.40 mg/kg before thrombectomy. The primary outcome was functional independence (modified Rankin Scale (mRS) of 0-2) at 90 days. Secondary outcomes included improvement over the full ordinal mRS scale, freedom of disability (mRS 0-1), mortality and occurrence of symptomatic intracranial haemorrhage. The average causal treatment effect was estimated via inverse probability of treatment weighting and G-Computation. We calculated standardised risk differences (SRDs) and adjusted (common) ORs (a(c)ORs). RESULTS: Of 377 patients included in the target trial, 187 received thrombectomy alone and 190 tenecteplase before thrombectomy. Tenecteplase before thrombectomy did not increase the probability of patients achieving functional independence (SRD 0.04 (95% CI -0.06 to 0.13)) but resulted in a significant improvement in the mRS overall (acOR 1.56 (95% CI 1.07 to 2.23)) and in a higher probability of freedom from disability (SRD 0.10 (95% CI 0.01 to 0.20)). The probability for improvement of functional outcomes was further increased in patients treated within 140 min after onset (ordinal mRS acOR 1.63 (95% CI 1.04 to 2.56)). No significant differences in safety outcomes were observed between the two groups. CONCLUSION: Tenecteplase before thrombectomy compared with thrombectomy alone did not increase the probability of functional independence but resulted in significant improvement over the full mRS scale. This improvement was most evident in patients treated early.
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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 | 0.102 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".