Behavioral Markers for Deficits in Speed of Processing in Cerebrovascular Disease
Bibliographic record
Abstract
Abstract Objective To assess overlap and uniqueness of established behavioral markers of speed of processing for different aspects of visual information within a cerebrovascular disease cohort, and to examine the link between these speed of processing markers and functional behavior, specifically walking. Methods A cohort of 161 participants with cerebrovascular disease recruited to the Ontario Neurodegenerative Disease Research Initiative (ONDRI) were examined with three types of assessments: neuropsychology, saccadic eye movement and gait. Principal component analysis (PCA) and canonical correlation analysis (CCA) were performed on select variables from these assessments to reveal commonalities and discrepancies among the measures. Results PCA analysis revealed different variable patterns between neuropsychology and saccade assessments, with the first component characterized primarily by neuropsychology, and the second and third components more influenced by the saccade assessments. CCA analysis did not reveal association between different types of assessments with the exception of a modest, but significant, positive association between speed of processing measures from the neuropsychological assessments and gait speed. Discussion Neuropsychological tests and the pro-saccade task can be used for assessment of speed of processing for two major features of visual information, visual perception vs. spatial location. Despite a general lack of association between different types of assessments, combining gait speed as an important contributor to the models reinforces the idea of the link between speed of processing and complex function such as walking, and provides support for the importance of attending to the potential consequences of changes in speed of processing after neurologic injury.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".