Revisiting Hemicraniectomy: Late Decompressive Hemicraniectomy for Malignant Middle Cerebral Artery Stroke and the Role of Infarct Growth Rate
Bibliographic record
Abstract
Objective and Methods.The outcome in late decompressive hemicraniectomy in malignant middle cerebral artery stroke and the optimal timings of surgery has not been addressed by the randomized trials and pooled analysis. Retrospective, multicenter, cross-sectional study to measure outcome following DHC under 48 or over 48 hours using the modified Rankin scale [mRS] and dichotomized as favorable ≤4 or unfavorable >4 at three months.Results.In total, 137 patients underwent DHC. Functional outcome analyzed as mRS 0–4 versus mRS 5-6 showed no difference in this split between early and late operated on patients [ P=0.140 ] and mortality [ P=0.975 ]. Multivariate analysis showed that age ≥ 55 years, MCA with additional infarction, septum pellucidum deviation ≥1 cm, and uncal herniation were independent predictors of poor functional outcome at three months. In the “best” multivariate model, second infarct growth rate [IGR2] >7.5 ml/hr, MCA with additional infarction, and patients with temporal lobe involvement were independently associated with surgery under 48 hours. Both first infarct growth rate [IGR1] and second infarct growth rate [IGR2] were nearly double [ P<0.001 ] in patients with early surgery [under 48 hours].Conclusions.The outcome and mortality in malignant middle cerebral artery stroke patients operated on over 48 hours of stroke onset were comparable to those of patients operated on less than 48 hours after stroke onset. Our data identifies IGR, temporal lobe involvement, and middle cerebral artery with additional infarct as independent predictors for early surgery.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".