Abstract T P120: Clinical Demographic Effects on Functional Outcome in Patients Following Decompressive Hemicraniectomy for Malignant Middle Cerebral Artery Infarction
Bibliographic record
Abstract
Background: Malignant MCA infarction is a devastating disease representing 1-10% of strokes. Evidence from recent randomized controlled trials show improved survival and functional outcomes following decompressive hemicraniectomy (DHC) compared with optimal medical. Ideal patient selection remains somewhat controversial. The current study examines the effects of demographic and clinical variables on functional outcomes in patients surviving one or more years following DHC. Methods: We retrospectively reviewed patients who underwent DHC for malignant MCA infarction at our institution from 03/2006 to 04/2012. We collected and compared demographic and clinical variables including age, gender, race, timing of DHC, side of infarction, IV TPA administration, and additional cerebral territorial involvement (ACA or PCA). The mean mRs was calculated for each group and the Wilcoxon-Mann-Whitney two tailed test was used to calculate statistical significance. Results: A total of 32 patients met inclusion/exclusion criteria. There was no statistical difference in functional outcomes between patients ≤60 years of age and those >60 years (p=0.51). No statistical difference was observed between males and females (p=0.84)). Patients who received their DHC within 48 hours of their stroke were more likely to have a better outcome than patients who received DHC after 48 hours (p=0.024). Other variables including race, cerebral dominance involvement, IV TPA administration, or additional cerebral territory involvement did not show statistical significance with respect to functional outcomes (p=0.22, p=0.462, p=0.597, and p=0.614 respectively). Conclusion: In this retrospective study, early DHC done within 48 hours of stroke was the only clinical variable shown to improve functional outcomes 1 year or more later among survivors of patients receiving DHC for malignant MCA infarction. No difference in functional outcome was seen based on age, gender, race, IV TPA administration, dominant MCA involvement, and additional cerebral territorial co infarction. Patient selection should be individualized and larger studies are needed to better assess this patient population, especially in the elderly.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".