The Validity of the Montreal Cognitive Assessment for Moderate to Severe Traumatic Brain Injury Patients
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to pilot the use of Montreal Cognitive Assessment as a quick clinical screen for cognitive assessment in traumatic brain injury patients. DESIGN: The study recruited 61 participants with moderate to severe traumatic brain injury presenting to a tertiary rehabilitation center under the Brain Injury Program. A Montreal Cognitive Assessment questionnaire and neuropsychological battery (Repeatable Battery for the Assessment of Neuropsychological Status and Color Trails Test) were administered to participants who had completed inpatient rehabilitation. RESULTS: Receiver operating characteristic analysis for the Montreal Cognitive Assessment revealed an optimal balance of sensitivity and specificity at 24/25 to discriminate participants who were classified as less than 5th centile on the Total Scale Index on the Repeatable Battery for the Assessment of Neuropsychological Status. This achieved a sensitivity, specificity, PPV, and NPV of 73.9%, 86.5%, 77.3%, and 84.2%, respectively. Receiver operating characteristic analysis for the trail making subtest of the Montreal Cognitive Assessment achieved a sensitivity, specificity, PPV, and NPV of 79.4%, 74.1%, 79.4%, and 74.1% in identifying patients classified as less than 5th centile on Color Trail Test part 2. CONCLUSIONS: The use of Montreal Cognitive Assessment displayed good validity in identifying patients with clinically significant impairment on a standard neuropsychological assessment battery in the study population. However, it may lack sensitivity for estimating mild levels of impairment.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".