Diagnostic efficacy of multimodal MRI combined with ICAM-1 for cognitive impairment after intracerebral hemorrhage
Bibliographic record
Abstract
Objective We aimed to investigate the diagnostic value of multimodal magnetic resonance imaging (MRI) combined with intercellular adhesion molecule-1 (ICAM-1) in detecting cognitive impairment (CI) after intracerebral hemorrhage (ICH).Methods Clinical data were collected from 130 patients with ICH, all of whom received brain MRI to assess imaging characteristics, including white matter lesion (WML) scores, the number of cerebral microbleeds (CMBs), fractional anisotropy (FA) values, and apparent diffusion coefficients. Serum ICAM-1 levels were detected. At a 3-month follow-up, patients were classified into a CI group (n = 57) and an non-CI group (n = 73) according to Montreal Cognitive Assessment. The diagnostic value of multimodal MRI parameters and ICAM-1 levels was analyzed. Logistic regression analysis was performed to identify independent predictors of CI after ICH.Results Patients in the CI group were older and had a higher prevalence of hypertension than the non-CI group. They also exhibited higher WML scores, greater CMB counts, and elevated serum ICAM-1 level, and lower FA values in the basal ganglia, parietal lobe, and frontotemporal regions. The combination of multimodal MRI parameters with ICAM-1 levels showed superior diagnostic accuracy compared to individual indicators. Logistic regression identified hypertension, WML score, basal ganglia FA value, parietal lone FA value, and ICAM-1 levels were independent factors of CI after ICH.Conclusion Multimodal MRI parameters and serum ICAM-1 levels have high diagnostic value for identifying CI after ICH. Hypertension, WML score, reduced FA value in specific brain regions, and elevated ICAM-1 levels are closely associated with CI after ICH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".