Diffusion tensor imaging and the Montreal cognitive assessment for assessing severe traumatic brain injury
Bibliographic record
Abstract
Objective To investigate any correlation between diffusion tensor imaging (DTI) results and Montreal cognitive assessment (MoCA) scores after severe traumatic brain injury (TBI).Methods Eight male pa-tients with chronic severe TBI were given the MoCA (including memory,attention,speech and executive function).DTI was used to quantify the fractional anisotropy (FA) of white matter fiber tracts in the radial and longitudinal fasciculus,under longitudinal fasciculus,internal capsule,corpus callosum genu and body,and the cingulate cortex.Pearson correlation coefficients were calculated to quantify the correlation between the FA values and MoCA scores.Results There was a positive correlation between FA in the corpus callosum body,corpus callosum genu and the superior longitudinal fasciculus and MoCA total scores.Conclusion The MoCA scores of patients with chronic severe TBI are related to white matter damage in the corpus callosum body,corpus callosum genu and the superior longitudinal fasciculus. Key words: Brain injury; Cognitive impairment; Montreal cognitive assessment; Diffusion tensor imaging ; Rehabilitation
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".