Utility of the Comprehensive Trail Making Test in the Assessment of Mild Cognitive Impairment in Older Patients
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study is to determine the usefulness of the CTMT (Comprehensive Trail Making Test) in diagnosing mild cognitive impairment in older patients. The test is used to assess executive functions, of which impairment is already observed in the early stages of the neurodegenerative process. MATERIALS AND METHODS: The study includes 98 patients of a geriatric ward assigned to 2 groups of 49 patients each: patients diagnosed with a mild cognitive impairment and patients without a cognitive impairment, constituting the control group (group K). A set of screening tests was used in the initial study: the MMSE (Mini-Mental State Examination), MoCA (Montreal Cognitive Assessment), and CDT (Clock Drawing Test), GDS (Geriatric Depression Scale). The second study included the performance of the CTMT; the performance indicator was the time of performance. RESULTS: < 0.01). Patients with MCIs took longer to complete all trails of the test. To identify cognitive impairment, cutoff points were proposed for the CTMT total score and the other test trails. The CTMT overall score and CTMT 5 scored the highest AUCs (CTMT overall score = 0.77, CTMT Trail 5 = 0.80). CONCLUSIONS: The Comprehensive Trail Making Test may be useful in diagnosing mild cognitive impairment as a complementary screening tool.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".