Sensitivity, specificity and cut-off point of the Montreal Cognitive Assessment (MoCA) in Patients with mild-Traumatic Brain Injury (mTBI)
Bibliographic record
Abstract
Background: Although patients with mild traumatic brain injury (mTBI) rarely exhibit an identifiable lesion on neuroimaging, they frequently experience neurocognitive problems. Objectives: The present study aimed to determine the cut-off point, sensitivity, and specificity of the Montreal Cognitive Assessment (MoCA) test in mTBI patients. Methods: In this cross-sectional-analytical study, the case group included 79 patients with mTBI were enrolled in the trauma, neurosurgery, and ICU ward of PourSina hospital (northern Iran), and there were 79 healthy individuals in the control group. Both groups were participating in this study were cognitively evaluated by the MoCA and MMSE test. Moreover, as retesting reliability and determining the concurrent and convergent validity of the MoCA, and Pearson correlation coefficient between two groups, MMSE test was performed on 20 mTBI patients with an average time interval of 3 days. The independent t-test, Cronbach’s alpha and discriminant analysis used for determining the distribution, internal consistency reliability and sensitivity, specificity, and diagnostic value of the MoCA test between groups respectively. Results: The results showed a cut-off point of 26/27 as the probable point of cognitive impairment in mTBI. Also, in order to identify cognitive impairment in mTBI patients, this test reported sensitivity of 0.62 and specificity of 0.81 with Youden's index of 0.43. Conclusion: In screening for possible mild cognitive impairment in mTBI patients, the MoCA is relatively useful and should not be used only as a substitute for a complete neuropsychological assessment with diagnostic purposes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".