Can flat-detector CT after successful endovascular treatment predict long-term outcome in patients with large vessel occlusion? An Alberta Stroke Programme Early CT Score–based study
Bibliographic record
Abstract
PURPOSE: Recent studies postulate a high prognostic value of the Alberta Stroke Programme Early CT Score (ASPECTS) applied on non-contrast whole-brain flat-detector CT (FDCT) after successful endovascular treatment (EVT). The aim of this study was the evaluation of long-term patient outcome after endovascular treatment using postinterventional FDCT. METHODS: Using a local database (Stroke Research Consortium in Northern Bavaria, STAMINA), 517 patients with successful endovascular treatment (modified Thrombolysis in Cerebral Infarction (mTICI) ≥ 2B) due to acute ischaemic stroke (AIS) and large vessel occlusion (LVO) of the anterior circulation were recruited retrospectively. In all cases, non-contrast FDCT after EVT was analysed with special focus at ASPECTS. These results were correlated with the functional outcome in long-term (modified Rankin Scale (mRS) shift from pre-stroke to 90 days after discharge). RESULTS: A significant difference in FDCT-ASPECTS compared to the subgroup of favourable vs. unfavourable outcome (Δ mRS) (median ASPECTS 10 (10-9) vs. median ASPECTS 9 (10-7); p = 0,001) could be demonstrated. Multivariable regression analysis revealed FDCT-ASPECTS (OR 0.234, 95% CI - 0.102-0.008, p = 0.022) along with the NHISS at admission (OR 0.169, 95% CI 0.003-0.018, p = 0.008) as independent factors for a favourable outcome. Cut-off point for a favourable outcome (Δ mRS) was identified at an ASPECTS ≥ 8 (sensitivity 90.6%, specificity 35%). CONCLUSION: For patients with LVO and successful EVT, FDCT-ASPECTS was found to be highly reliable in predicting long-term outcome.
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 | 0.003 |
| 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 | 0.001 | 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".