P.063 Thrombolysis for acute ischemic stroke in patients with pre-morbid disability: a meta-analysis
Bibliographic record
Abstract
Background: Randomized-controlled trials of thrombolysis in ischemic stroke have poorly represented patients with pre-stroke disability and the benefit of thrombolysis in this population remains uncertain. We performed a systematic review and meta-analysis to examine the outcomes of thrombolysis in patients with pre-morbid disability. Methods: In accordance with MOOSE guidelines, we retrieved studies reporting intravenous thrombolysis (IVT) in patients with pre-stroke disability (mRS=3-5) with ischemic stroke, either compared to untreated patients or to treated patients without pre-morbid disability. Primary outcome was the return to pre-morbid disability at 90-days. Results: 8 articles were included involving 103,988 patients. Patients with disability treated with IVT had better odds of returning to baseline function compared to those who did not receive IVT (OR=7.26, 95%CI=2.51-21.02). Mortality and sICH were not significantly different between patients with disability receiving IVT or not. Favourable outcomes (mRS=0-2 or return to pre-morbid mRS) and sICH were not significantly different between patients with and without disability. Mortality was three times higher in those with pre-morbid disability treated with IVT (38.2% versus 12.6%). Conclusions: Thrombolysis in patients with disability was associated with better outcomes compared to patients not receiving IVT. High-quality data comparing treated versus untreated patients with pre-morbid disability is needed to clarify this issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.059 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".