Development of a Diagnostic Prediction Model for Post‐Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol
Bibliographic record
Abstract
OBJECTIVE: Stroke is a leading cause of morbidity and disability worldwide. Post-stroke cognitive impairment (PSCI) significantly affects long-term prognosis in acute anterior circulation large-vessel occlusion stroke (LVO-AIS). This study aims to develop a PSCI prediction model integrating multimodal imaging, demographic, and clinical data collected during hospitalization. METHODS AND DESIGN: This single-center, prospective cohort study will enroll 379 anterior circulation LVO-AIS patients undergoing emergency endovascular treatment (EVT) within 24 h of symptom onset. Participants will be categorized into PSCI and non-PSCI groups and followed up at 90 and 180 days post-procedure. Primary outcomes include Montreal Cognitive Assessment scores at 3 and 6 months, with the modified Rankin Scale as a secondary outcome. Baseline imaging data will be processed using 3D Slicer for MRI and PET/CT standardization, registration, and feature extraction. Machine learning models will be developed using these imaging features combined with demographic and clinical data and evaluated via metrics such as the area under the receiver operating characteristic curve, precision, and recall. Analyses will be conducted in a blinded manner. CONCLUSION: This study will develop a PSCI prediction model based on multimodal imaging and clinical data in EVT-treated LVO-AIS patients, providing a tool for early diagnosis and personalized interventions. While limited to a single-center, future multicenter validation is necessary to establish its generalizability and clinical utility.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".