Abstract TP460: Dominant Imaging Markers for Post Stroke Cognitive Performance in the TABASCO Study
Bibliographic record
Abstract
Introduction: Post-stroke patients are at high risk of developing cognitive decline. Previous studies have examined only limited magnetic resonance imaging (MRI) parameters for prediction of cognitive status following stroke Aim: To determine radiological markers associated with different cognitive domains in post-stroke patients. Materials and Methods: Patients from the TABASCO (Tel-Aviv Brain Acute Stroke Cohort), a prospective cohort of first-ever mild-moderate ischemic stroke patients, were Included (N=141). All patients underwent a 3T MRI and were cognitively assessed at admission and 2 later. Multiple regression models were used to assess which of the imaging measures best associated with cognitive scores 2 years after the index event. Results: After controlling for both age and education, the comparisons between cognitively intact and impaired patients revealed significant differences between the two groups in relative CSF volume, white matter lesion (WML) load and white matter microstructural integrity, reflected as mean diffusivity (MD), radial diffusivity (Dr) and axial diffusivity (Da) values ( p’s <0.05). Multiple regression analyses demonstrated the significant role of hippocampi MD to serve as a common predictor for all three cognitive scores. Relative WML load significantly contributed to the global score and executive function. Discussion: Our results highlight the role of hippocampal MD and WML load as markers for cognitive performances. We suggest that these imaging markers best demonstrated "brain intergrity"
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".