Predicting intracerebral hemorrhage after endovascular therapy for anterior circulation strokes using CT-ASPECT, CTP-ASPECT and DWI-ASPECT: Protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Over the last decade, there have been significant advances in treatments for anterior ischemic stroke, most notably endovascular thrombectomy (EVT). Despite the success of EVT on overall outcomes, intracerebral hemorrhage (ICH) is an important post-procedure complication, often associated with mortality and disability. Hence, predicting the risk of ICH can inform EVT decision making. The ASPECT score is used globally to predict patients' prognosis post-reperfusion therapy. Our objective is to perform a systematic review to collect and synthesize data on the association between ASPECT scores on CT, CTP and DWI-MRI (CT-ASPECT, CTP-ASPECT, and DWI-ASPECT) and the risk of symptomatic ICH after EVT for anterior circulation strokes. METHODS AND ANALYSIS: We will conduct a broad search of various electronic databases (MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, and the Cochrane Database of Systematic Reviews) to identify studies published after January 1st, 2012 (commonly accepted as the beginning of the modern EVT era based on availability of stent-retrievers). Two independent reviewers will screen and include studies evaluating associations between symptomatic ICH after thrombectomy and baseline CT-ASPECT, CTP-ASPECT and DWI-ASPECT scores. Data will be extracted to quantify the risk of sICH after EVT based on the ASPECT scoring. TRIAL REGISTRATION: PROSPERO registration number: CRD42023459860.
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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 | 0.045 | 0.052 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.021 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".