Ten-words recall test: an effective tool to differentiate mild cognitive impairment from subjective cognitive decline
Bibliographic record
Abstract
Introduction Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are stages 2 and 3, respectively, of the Alzheimer’s continuum. The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-cog’s) ten-words recall test is a validated method for the early detection of cognitive impairment in Alzheimer’s disease. However, limited studies have investigated its ability to differentiate between SCD and MCI. Methods 203 participants with SCD and 62 participants with MCI underwent multiple neuropsychological assessments. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment-Basic (MOCA-B) served as brief global cognition tests. A binary logistic regression model was used to analyze the potential factors affecting MCI. The accuracy of the ten-words recall test was assessed using the area under the receiver operating characteristic (ROC) and the area under the curve (AUC). Results The neuropsychological assessment revealed significant differences in the ten-words recall test scores between the SCD (median age 61 years; 70.44% female) and MCI (median age 64 years; 61.29% female) groups (p < 0.001), with the MCI group scoring the highest. Using a cut-off value of 3.15 for the ten-words recall test, sensitivity for distinguishing MCI from SCD reached 87%, while specificity stood at 61% (AUC 0.777, p < 0.001). DeLong’s test indicated no statistically significant difference in the ten-words recall test’s ability to distinguish between SCD and MCI compared to the total score of ADAS-Cog (AUC 0.833, p) and MMSE (AUC 0.784, p > 0.05). However, a significant difference was observed when compared to MoCA-B (AUC 0.973, p < 0.001). In the population with an education level of ≤ 9 years, the optimal cut-off value for the ten-words recall test was 3.15, yielding a sensitivity of 91% and a specificity of 45% (AUC = 0.674, p = 0.030). In the population with an education level > 9 years, the optimal cut-off value was 3.63, resulting in a sensitivity of 79% and a specificity of 71% (AUC = 0.785, p < 0.001). Discussion The ten-words recall test from the ADAS-cog may detect MCI early owing to its simplicity and quick administration. It is an effective and convenient tool for rapidly identifying mild cognitive impairment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".