Validation of neuropsychological tests for the China Health and Retirement Longitudinal Study Harmonized Cognitive Assessment Protocol
Bibliographic record
Abstract
OBJECTIVE: To compare and validate neurocognitive tests in the Harmonized Cognitive Assessment Protocol (HCAP) for the China Health and Retirement Longitudinal Study (CHARLS), and to identify appropriate tests to be administered in future waves of CHARLS. METHODS: We recruited 825 individuals from the CHARLS sample and 766 subjects from hospitals in six provinces and cities in China. All participants were administered the HCAP-neurocognitive tests, and their informants were interviewed regarding the respondents' functional status. Trained clinicians administered the Clinical Dementia Rating scale (CDR) to assess the respondents' cognitive status independently. RESULTS: The testing protocol took an average of 58 minutes to complete. Refusal rates for tests of general cognition, episodic memory, and language were less than 10%. All neurocognitive test scores significantly correlated with the CDR global score (correlation coefficients ranged from 0.139 to 0.641). The Mini-Mental State Examination (MMSE), the Health and Retirement Study (HRS) - telephone interview for cognitive status (TICS), community screening instrument for dementia (CSI-D) for respondent, episodic memory and language tests each accounted for more than 20% of the variance in global CDR score (p < 0.001) in bivariate tests. In the CHARLS subsample, age and education were associated with neuropsychological performance across most cognitive domains, and with functional status. CONCLUSION: A brief set of the CHARLS-HCAP neurocognitive tests are feasible and valid to be used in the CHARLS sample and hospital samples. It could be applied in the future waves of the CHARLS study, and it allows estimating the prevalence of dementia in China through the population-based CHARLS.
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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 | 0.077 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".