CUT OFF VALUE OF GOOD PRONOSTIC FACTOR OUTCOMES IN LARGE TERRITORY ISCHEMIC STROKE UNDERGOING EARLY DECOMPRESSIVE CRANIECTOMY
Bibliographic record
Abstract
Background: Decompressive craniectomy (DC) significantly reduces mortality in large territory ischemic strokes that develop intractable cerebral edema. However, evidence for functional benefit remains sparse and contradictory. Objective: This study aimed to assess cut-off value for predictor outcomes of early DC. Methods: We conducted a prospective, observational cohort study from December 2016 to June 2021. Patients were screened for ischemic stroke involving the middle cerebral, internal carotid artery or both using the National Institutes of Health Stroke Scale score. All patients underwent DC. Multivariate analysis was performed for an array of clinical variables in relation to functional outcomes according to the modified Rankin Scale (mRS) and Pearson’s correlation coefficient analysis. Clinical outcome was assessed after 3- and 6-month follow-up. Results: In total, 243 patients were included in this study. Age ≤71 years (AUC=0.955, p <0.001 accuracy 89.7%), onset to DC ≤9 hours (AUC=0.824, p <0.001 accuracy 78.8%), volume of infarction ≤155 cm3 (AUC=0.939, p <0.001 accuracy 93.6%) and the Alberta Stroke Program Early CT Score or ASPECT score ≥6 (AUC = 1, p <0.001 accuracy 100%) were significantly associated with good clinical outcomes in early DC (mRS 0 to 3). Conclusion: Among patients with large territory ischemic strokes undergoing early DC, age ≤71 years, onset to DC ≤9 hours, volume of infarction ≤155 cm3 and ASPECT score ≥6 was significantly associated with good clinical outcomes. All prognostic factors in early DC correlated well with functional outcomes at 6 months which could be used to predict outcome, and consider clinical indications and informed postoperative complications among patients with large territory ischemic stroke.
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 | 0.004 |
| 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 | 0.001 | 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".