Validation of the Ten‐word Test for Immediate Memory in Middle‐Aged and Older Population with Subjective Cognitive Decline and Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: To validate the ten-word test for immediate memory from the Alzheimer's Disease Assessment Scale-Cognitive section (ADAS-Cog) in detecting subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHOD: 79 people aged 38∼80 years old participated in the study and they were classified as SCD (n = 55) or MCI (n = 24). RESULT: The the Mann-Whitney U test showed that the ten-word test score was significantly different in SCD compared to MCI (p = 0.001, Figure 1). In particular, a cut-off score of 4.15 points had an 63% sensitivity and 87% specificity for discriminating MCI from SCD (AUC = 0.73, p = 0.001), while a cut-off score of 4.15 also had a sensitivity of 79% and a specificity of 83% for discriminating MCI from SCD in age > 60 years old adults (AUC = 0.82, p = 0.001), and a cut-off score of 4.35 points had a sensitivity of 90% and a specificity of 95% for discriminating MCI from SCD in those age > 60 years old adults with education level > 9 years (AUC = 0.93, p < 0.001) (Figure 2-3). Convergent validity was found between the ten-word test and ADAS-cog total scores (r = 0.56, p < 0.001), and the Mini-Mental State Examination (MMSE) (r = -0.28, p = 0.013) and the Montreal Cognitive Assessment-Basic (MOCA-B) (r = -0.20, p = 0.078). CONCLUSION: The ten-word test for immediate memory from the ADAS-Cog is a good to fair screening tool with adequate discriminant validity for administration in people with SCD and MCI.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".