Endovascular treatment versus medical management for basilar artery occlusion with low-to-moderate symptoms (National Institutes of Health Stroke Scale < 10)
Bibliographic record
Abstract
Abstract Background: Patients with acute basilar artery occlusion (BAO) and low-to-moderate symptoms (National Institutes of Health Stroke Scale [NIHSS] < 10) are poorly represented in thrombectomy trials. Our objective is to compare thrombectomy and best medical management (BMT) in this population. Methods: We compared data of all consecutive patients presenting with an initial NIHSS < 10 and acute symptomatic BAO included in two registries. The main outcome was the proportion of patients achieving a 3-months favorable outcome (mRS 0-2 or equal to the pre-stroke value). Secondary outcomes included the proportion of patients with an excellent outcome (mRS 0-1 or equal to pre-stroke value), overall mRs distribution (shift analysis) and mortality. Effect sizes for thrombectomy versus BMT alone were calculated using binary or ordinal logistic regression model before after considering confounders using the inverse probability of treatment weighting (IPTW) propensity score method. Results: One hundred twenty-seven patients were included: sixty-four patients treated with thrombectomy (mean ± SD age: 63.4 ± 16.1) and sixty-three with BMT (mean ± SD age: 69.0 ± 14.3). There was no significant difference between groups for the rate of 3 month-favorable outcome or mortality. After propensity-score adjustment, thrombectomy was associated with a significantly higher chance of excellent outcome at 3 months (mRS 0-1 or equal to pre-stroke value; adjusted OR, 2.68; 95%CI, 1.04–6.90; p = 0.041). Conclusion: Our study suggests that thrombectomy in patients with low-to-moderate symptoms (NIHSS < 10) due to BAO does not improve the rate of favorable outcome but could lead to a higher chance of excellent outcome at 3 months. Trial Registration: ETIS Registry. http://www.clinicaltrials.govNCT03776877
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".