Predictors of favorable functional outcomes for elderly patients undergoing endovascular thrombectomy for acute ischemic stroke
Bibliographic record
Abstract
PURPOSE: The aim of this study was to identify factors that predict favorable functional outcomes in elderly patients with large-vessel occlusion acute ischemic stroke (LVO-AIS) who underwent mechanical thrombectomy (MT). METHODS: We conducted a retrospective observational study using the prospectively maintained Bigdata Observatory for Stroke of China (BOSC) to identify eligible patients who underwent MT for LVO-AIS at four comprehensive stroke centers between August 2019 and February 2022. Inclusion criteria included patients aged 80 years or older with a baseline modified Rankin Scale (mRS) 0-2, baseline National Institutes of Health Stroke Scale (NIHSS) > 6, baseline Alberta Stroke Program Early CT Score (ASPECTS) > 6 who received treatment within 24 h from symptom onset. Pertinent demographic, clinical, and procedural variables were collected. Multivariable regression analyses were performed to identify predictors of favorable long-term functional outcomes, defined as mRS 0-2 at 90 days. RESULTS: A total of 63 patients were included in the study with a mean age of 83 years. Patients with previous diagnosis of atrial fibrillation were more likely to have a favorable functional outcome (OR 2.09, 95% CI 2.09-407.33, p = 0.012), while a higher baseline NIHSS was associated with a less favorable functional outcome (OR 0.64, 95% CI 0.46-0.89, p = 0.007). In addition, there was an observed trend suggesting an association between higher baseline ASPECTS and favorable functional outcomes. This association did not reach statistical significance (OR 2.49, 95% CI 0.94-6.54, p = 0.065). CONCLUSION: In this study, we identified factors that predicted a favorable functional outcome in elderly LVO-AIS patients undergoing MT. A higher baseline NIHSS decreased the odds of mRS 0-2 at 90 days, whereas a history of atrial fibrillation increased the odds of a favorable functional outcome. These results emphasize the complex relationship between clinical factors and functional recovery in this vulnerable population.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".