Mechanical thrombectomy in patients with acute ischemic stroke and ASPECTS ≤6: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: It is uncertain whether mechanical thrombectomy (MT) increases the probability of a good outcome (modified Rankin Scale (mRS) 0-2) in patients with Alberta Stroke Program Early CT Score (ASPECTS) 0-6. OBJECTIVE: To assess the impact of MT in patients with pretreatment ASPECTS 0-6. METHODS: According to PRISMA guidelines, we performed a systematic search of three databases for series of patients with ASPECTS 0-6 treated by MT. Random-effects meta-analysis was used to pool the following: rate of mRS 0-2 at 3 months follow-up, symptomatic intracranial hemorrhage (sICH), and mortality rates. RESULTS: We included 17 studies and 1378 patients with ASPECTS 0-6 (1194 MT, 184 medical management). The rate of mRS 0-2 was 30.1% and 3.2% after MT and medical management, respectively. MT gave higher odds of mRS 0-2 (OR 4.76, p=0.01). Patients with ASPECTS 6 and 5 had comparable rates of good outcome (37.7% and 33.3%, respectively). Overall, the rate of mRS 0-2 was 17.1% in patients with ASPECTS 0-4: 22.1% and 13.9% of patients with ASPECTS 4 and 0-3 were functionally independent, respectively. Successful recanalization (Thrombolysis in Cerebral Infarction grade 2b-3) gave higher odds of mRS 0-2 than unsuccessful reperfusion (OR 5.2, p=0.001). The MT group tended to have lower odds of sICH compared with the controls (OR 0.48, p=0.06). Patients aged <70 years had higher rates of mRS 0-2 than those aged >70 years (40.3% vs 16.2%). CONCLUSIONS: Patients with ASPECTS 0-6may benefit from MT. Successful reperfusion increases the probability of 3-month functional independence without increasing the risk of sICH. Patients with ASPECTS 5 and 6 have comparable outcomes. MT can still enable approximately one in four patients with ASPECTS 4 to be independent, whereas only 14% of subjects with ASPECTS 0-3 regain a good functional 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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".