Predictors of Decompressive Hemicraniectomy in Successfully Recanalized Patients With Anterior Circulation Emergency Large‐Vessel Occlusion
Bibliographic record
Abstract
Background Mechanical thrombectomy (MT) has been shown to improve functional outcome in patients with anterior circulation strokes and emergent large‐vessel occlusion (ELVO). Despite successful recanalization, some of these patients require decompressive hemicraniectomy (DHC). We aimed to study the predictors of DHC in successfully recanalized anterior circulation ELVO patients. Methods Consecutive patients with anterior circulation ELVO treated with MT during a 6‐year period were evaluated. Only successfully recanalized patients (modified Thrombolysis in Cerebral Infarction grades 2b, 2c, or 3) after MT were included in the analysis. Baseline demographic, clinical, and procedural variables were compared between patients requiring DHC after successful recanalization versus those who did not. Results Of 453 successfully recanalized patients with ELVO, 47 who underwent DHC had higher admission blood glucose levels (170±88 versus 142±66 mg/dL; P =0.008), lower median Alberta Stroke Program Early CT Scores (9 [interquartile range, 8–10] versus 10 [interquartile range, 9–10]; P =0.002), higher prevalence of poor collaterals on pretreatment computed tomography angiogram (75% versus 26%; P <0.001), and required more passes during MT (median, 3 [interquartile range, 3–4] versus 2 [interquartile range, 1–2]; P =0.001) compared with those who did not undergo DHC. In a multivariable model after adjusting for multiple confounders, higher admission blood glucose levels ( P =0.031), poor collaterals on computed tomography angiography ( P <0.001), and higher number of passes during MT ( P <0.001) emerged as independent predictors of DHC in successfully recanalized patients with ELVO. Conclusions Higher admission blood glucose levels, poor collateral pattern on computed tomography angiography, and higher number of passes during MT were independently associated with DHC in patients with anterior circulation ELVO achieving successful recanalization following MT.
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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 | 0.000 | 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 | 0.001 | 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".