Abstract P384: Outcomes of Endovascular Therapy in Patients With Pre-Stroke Mobility Impairment
Bibliographic record
Abstract
Background and Purpose: Patients with pre-stroke mobility impairment were excluded from endovascular clinical trials. There is limited data regarding safety and outcomes of endovascular thrombectomy (EVT) in this population. We used a large, national dataset (Get With The Guidelines (GWTG)-Stroke) to evaluate the safety and outcomes of EVT in patients with pre-stroke mobility impairment (PSMI). Methods: We included patients who underwent EVT in the GWTG-Stroke registry between 2015 and 2019. PSMI was defined as inability to ambulate independently and poor outcome was defined as in-hospital mortality or discharge to hospice. GEE logistic regression models were used to evaluate the association between PSMI and outcomes. Results: Of 56,762 patients treated with EVT, 2919 (5.14%) had PSMI. Patients with PSMI were older (median 79 [IQR 70-87] vs 70 [59-80], P<0.001), more likely to be female (63.4% vs 49.2%, P<0.001), had more medical comorbidities, presented with a higher NIHSS (19 [12-24] vs 15 [9-21], P<0.001), and were less likely to be treated with tPA (36.8% vs 45.6%, P<0.001). PSMI was not associated with intracranial hemorrhage but was associated with poor outcome (Table 1). Patients with PSMI with poor outcomes were more likely to be older (83 [74-89] vs 77 [68-86], P<0.001) and have a higher presenting NIHSS (21 [16-25] vs 16 [11-22], p<0.001). Forty-nine percent of patients with PSMI with age >80 years and NIHSS >20 had a poor outcome. Conclusions: Amongst patients with PSMI treated with EVT, two thirds survived and one third were discharged to home or to inpatient rehabilitation. Advanced age and increased stroke severity increased the likelihood of poor outcomes. EVT appears safe in patients with PSMI, yet further study of effectiveness in this population is warranted.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".