Validation of T-MoCA in the screening of mild cognitive impairment in Chinese patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Background: Atrial fibrillation (AF) is associated with high risk of mild cognitive impairment (MCI) and dementia. However, feasible and simple instruments that facilitates regular assessment of cognitive status in AF patients remain underdeveloped. Methods: Cognitive function of 136 AF patients was first evaluated using T-MoCA. Cognitive function of 101 patients was then assessed through in-person interview by physicians who are blinded to telephone interview results, using both Clinical Dementia Rating (CDR) and Mini-Mental Status Evaluation (MMSE). Using CDR=0.5 as a reference standard, the ability of T-MoCA and MMSE to discriminate cognitive dysfunction, stratified by education level, was tested by receiver-operating curve (ROC) analysis. Net reclassification index was calculated for comparison between the performance of T-MoCA and MMSE. Results: Thirty-five MCI patients were identified as MCI using the criteria of CDR=0.5. The areas under the ROC curve of T-MoCA were 0.80 (0.71-0.89), 0.83 (0.71-0.95), and 0.85 (0.64-0.92) for all patients, patients with high educational level, and patients with low education level, respectively. The optimal threshold was achieved at 16/17 with a sensitivity of 85.7% and a specificity of 69.7% in overall patients, 15/16 with a sensitivity of 88.2% and a specificity of 64.5% in the low educational level patients, and 16/17 with a sensitivity of 77.8% and a specificity of 87.9% in the high educational level patients. Compared to the criterion MMSE≤27 and MMSE norms for the Chinese community elderly, stratified T-MoCA threshold improves correct classification by 23.7% (p=0.033) and 30.3% (p=0.020) respectively. Conclusion: T-MoCA is a feasible and effective method for MCI screening in patients with AF.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".