Differences in cognitive profiles between traumatic brain injury and stroke: A comparison of the Montreal Cognitive Assessment and Mini-Mental State Examination
Bibliographic record
Abstract
PURPOSE: To investigate the profiles of cognitive impairment through Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) in patients with chronic traumatic brain injury (TBI) or stroke and to evaluate the sensitivity of the two scales in patients with TBI. METHODS: In this cohort study, a total of 230 patients were evaluated, including TBI group (n = 103) and stroke group (n = 127). The cognitive functions of two groups were evaluated by designated specialists using MoCA (Beijing version) and MMSE (Chinese version). RESULTS: Comparedwith the patientswith stroke, the patientswith TBI received significantly lower score in orientation subtest and recall subtest in both tests.MoCA abnormal rates in the TBI group and stroke group were 94.17% and 86.61% respectively,whileMMSE abnormal rateswere 69.90% and 57.48%, respectively. In the TBI group, 87.10% patientswith normalMMSE score had abnormalMoCA score and in the stroke group, about 70.37% patients with normal MMSE score had abnormal MoCA score. The diagnostic consistency of two scales in the TBI group and the stroke group were 72% and 69%, respectively. CONCLUSION: In our rehabilitation center, patients with TBI may have more extensive and severe cognitive impairments than patients with stroke, prominently in orientation and recall domain. In screening post- TBI cognitive impairment, MoCA tends to be more sensitive than MMSE.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".