A-72Clinical Utility of the Montreal Cognitive Assessment (MoCA) in Brain Injury Rehabilitation: Association of MoCA Total and Subtest Scores with Self-Reported Brain Injury Symptoms on the Problem-Checklist (PCL)
Bibliographic record
Abstract
Objective: Although the MoCA has gained tremendous popularity, validation studies for its use in brain-injured (ABI) patients are limited. This is the first study to determine the utility of the MoCA in ascertaining the extent of cognitive impairment in ABI patients, and how it relates to self-reported cognitive, physical and affective symptoms using the Problem Checklist (PCL). Method: One hundred and nine ABI patients referred for physiatry assessment in a rehabilitation outpatient hospital clinic completed both the MoCA and the 43-item PCL at their initial visit. Anova, correlational and qualitative analyses were conducted to determine the relationship between MoCA scores and the frequency and severity of self-reported ABI symptoms. Results: MoCA scores ranged from 7 to 30, with the lower quartile falling at 21, and the upper quartile falling at 26. Patients with more significant cognitive impairment reported more ABI symptoms, particularly physical symptoms. Although all patient groups reported some cognitive difficulties, a greater proportion of patients with normal MoCA performance reported problems with memory and distractibility. Depression was more frequently reported by patients with either severe cognitive difficulties or with no cognitive impairment. The Abstraction and Delayed Recall items on the MoCA were less well performed and distinguished patients with minimal cognitive impairment. In contrast, difficulties on the Orientation items depicted more severe cognitive impairment. Conclusion: The findings of this study are generally consistent with those of previous research. The MoCA appears to be an efficient cognitive screening tool and its utility in determining treatment options for ABI patients will be discussed.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".