Cognitive Function and Prognosis of Multimodal Neuroimage-Guided Thrombectomy on Mild to Moderate Anterior Circulation Infarction Patients with Broadened Therapeutic Window: A Prospective Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Endovascular mechanical thrombectomy is an important approach for acute ischemic stroke (AIS) treatment. Multimodal neuroimaging methods ideally provide the exact localization, extent, and metabolic activity of target tissues. Post-stroke cognitive impairment has recently been realized to be another major concern except for neurological function impairment. The aim of our study was to carry out a prospective study to compare neurological and cognitive functions after thrombectomy in mild to moderate anterior circulation infarction patients selected by multimodal neuroimaging. METHODS: Ninety patients were recruited from January 2016 to March 2017 consecutively. Neurological function was assessed by NIHSS before thrombectomy, and 6 h, 24 h, 7 days, 90 days after mechanical thrombectomy. Cognitive functions were evaluated by Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Hachinski Ischemic Scale. RESULTS: Patients who received mechanical thrombectomy had significantly better neurological functions at 6 h (p < 0.001), 24 h (p < 0.001), 7 days (p < 0.001), and 90 days (p < 0.001), as well as cognitive functions evaluated by MoCA (26.23 ± 3.85 vs. 24.62 ± 2.25, p = 0.022, n = 85) and MMSE (26.65 ± 2.77 vs. 25.10 ± 2.36, p = 0.023, n = 85) compared to the standard therapy group. CONCLUSIONS: The current prospective study demonstrated that mechanical thrombectomy can significantly improve neurological and cognitive functions in patients with mild to moderate AIS at broadened therapeutic window under multimodal CT and multimodal MRI imaging.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".