The Chinese version of the Short Cognitive Performance Test (SKT) – psychometric criteria and cross-validation
Bibliographic record
Abstract
The new Chinese version of the SKT (Syndrom-Kurz-Test) Short Cognitive Performance Test aims to detect early cognitive impairment and is a promising addition to neuropsychological test batteries in China and suitable for Chinese speaking patients. This study has three aims: 1. to assess whether the SKT’s diagnostic accuracy is comparable to established cognitive impairment tests (e.g. Addenbrooke´s cognitive examination-III (ACE-III) and Montreal Cognitive Assessment-Basic (MoCA-B)); 2. to examine whether the three tests show intra-individual differences according to different types of mild cognitive impairment (single-domain amnestic MCI, semantic MCI and multiple-domain amnestic MCI); 3. to determine whether these tests distinguish between individuals with no cognitive impairment and those with subjective cognitive decline (SCD). The validation sample included 1038 older adults (mean age = 70.04, SD = 6.05) from the Chinese Preclinical Alzheimer’s Disease Study (C-PAS). Participants underwent cognitive testing and received consensus diagnoses of normal cognition, MCI, or dementia. Sensitivity and specificity were calculated for each test. ANOVAs examined differences between MCI subtypes, and t-tests assessed group differences between normal cognition and SCD. Results: The SKT showed the highest discrimination between normal cognition and cognitive impairment (MCI or dementia), with a sensitivity of 85.4% and specificity of 69.8%. All three tests demonstrated significant score differences across MCI subtypes. Additionally, all tests significantly distinguished individuals with SCD from those without cognitive impairment (p < 0.01). Conclusions: The new Chinese produced weaker results than the established tests. However, given its strengths, it could be a useful tool for identifying cognitive impairment in certain situations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".