The role of infarct location in patients with DWI-ASPECTS 0–5 acute stroke treated with thrombectomy
Bibliographic record
Abstract
Objective To determine whether hemisphere involvement and infarct location on the Alberta Stroke Program CT Score (ASPECTS) template should serve as predictors of 90-day clinical outcome in patients with acute ischemic stroke with pretreatment diffusion-weighted imaging (DWI)–ASPECTS 0–5 treated with mechanical thrombectomy (MT). Methods We analyzed data of all consecutive patients included in the Endovascular Treatment in Ischemic Stroke registry between January 1, 2012, and August 31, 2018, who presented with a pretreatment DWI-ASPECTS 0–5 and underwent MT. Multivariable analyses were performed in order to identify the role of infarct location and hemisphere involvement on good outcome defined by a modified Rankin Scale (mRS) score 0–2 at 90 days and on the whole distribution of mRS (shift analysis). Results A total of 344 patients with a DWI-ASPECTS 0–5 (median 4, IQR 3–5) were included. Neither infarct location nor hemisphere involvement was found to be an independent predictor of good outcome. Involvement of the M6 region in right-sided strokes (adjusted odds ratio [aOR] 2.6, 99% confidence interval [CI] 1.14–5.8; p = 0.003) and the internal capsule in left-sided strokes (aOR 2.6, 99% CI 0.8–7.9; p < 0.020) independently predicted increased disability on the mRS distribution in the affected subpopulations. Conclusion Our study suggests that neither hemisphere nor infarct location should be considered as an exclusion criterion for MT in patients with stroke with pretreatment DWI-ASPECTS 0–5. The involvement of specific regions of interest was associated with increased disability. These may provide valuable information regarding stroke management options and neurologic recovery for use of caregivers in the postacute phase.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".