Validity of the Montreal Cognitive Assessment for traumatic brain injury patients with intracranial haemorrhage
Bibliographic record
Abstract
UNLABELLED: BACKGROUND AND PRIMARY OBJECTIVE: In recent years, the Montreal Cognitive Assessment (MoCA) has been developed to assess patients with ischemic stroke. However, it has not been validated for use on traumatic brain injury patients with intracranial haemorrhage (tICH). The aim was to evaluate the psychometric properties of the MoCA (MoCA) in such patients. RESEARCH DESIGN AND METHOD: A cross-sectional observational study was carried out on 40 controls and 48 tICH patients recruited in Hong Kong. Concurrent validity was assessed by a comprehensive battery of neuropsychological tests and the Mini-Mental State Examination (MMSE). Criterion validity was assessed by the differentiation of tICH patients from controls. MAIN OUTCOME AND RESULTS: In tICH patients, cognitive z-scores (β = 0.579; p < 0.001) and MMSE (β = 0.366, p = 0.012) significantly correlated with performance in the MoCA after adjustment for age, gender and total score for the Geriatric Depressive Scale. For the differentiation of tICH patients from controls, analysis of receiver operating characteristics curves in the MoCA revealed an optimal balance of sensitivity and specificity at 25/26 with an area under the curve of 0.704 (p = 0.001). MoCA is applicable to and significantly correlated with excellent neurological outcomes in tICH patients. CONCLUSIONS: MoCA is a useful and psychometrically valid tool for the assessment of gross cognitive function in tICH patients.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".