Outcome Predictors in Acute Basilar Artery Occlusion
Bibliographic record
Abstract
OBJECTIVE: to identify predictors of good outcome in acute basilar artery occlusion (Bao). Background: acute ischemic stroke (aiS) caused by Bao is often associated with a severe and persistent neurological deficit and a high mortality rate. METHODS: the set consisted of 70 consecutive aiS patients (51 males; mean age 64.5 ± 14.5 years) with Bao. the role of the following factors was assessed: baseline characteristics, stroke risk factors, pre-event antithrombotic treatment, neurological deficit at time of treatment, estimated time to therapy procedure initiation, treatment method, recanalization rate, change in neurological deficit, post-treatment imaging findings. 30- and 90-day outcome was assessed using the modified rankin scale with a good outcome defined as a score of 0– 3. RESULTS: the following statistically significant differences were found between patients with good versus poor outcomes: mean age (54.2 vs. 68.9 years; p=0.0001), presence of arterial hypertension (52.4% vs. 83.7%; p=0.015), diabetes mellitus (9.5% vs. 55.1%; p=0.0004) and severe stroke (14.3% vs. 65.3%; p=0.0002), neurological deficit at time of treatment (14.0 vs. 24.0 median of national institutes of health Stroke Scale [nihSS] points; p=0.001), successful recanalization (90.0% vs. 54.2%; p=0.005), change in neurological deficit (12.0 vs. 1.0 median difference of nihSS points; p=0.005). Stepwise binary logistic regression analysis identified age (or=0.932, 95% Ci=0.882–0.984; p=0.012), presence of diabetes mellitus (or=0.105, 95% Ci=0.018-0.618; p=0.013) and severe stroke (or=0.071, 95% Ci=0.013-0.383; p=0.002) as significant independent negative predictors of good outcome. CONCLUSIONS: in the present study, higher age, presence of diabetes mellitus and severe stroke were identified as significant independent negative predictors of good outcome.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".