The Efficacy of General Practitioner Assessment of Cognition in Chinese Elders Aged 80 and Older
Bibliographic record
Abstract
OBJECTIVES: This study examined the efficacy of the General Practitioner Assessment of Cognition-Chinese version (GPCOG-C) in screening dementia and mild cognitive impairment (MCI) among older Chinese. METHODS: Survey questionnaires were administered to 293 participants aged 80 or above from a university hospital in mainland China. Alzheimer disease and MCI were diagnosed in light of the National Institute on Aging and the Alzheimer's Association (NIA/AA) criteria. The sensitivity and specificity of GPCOG-C and Mini-Mental State Examination (MMSE) in screening dementia and MCI were compared to the NIA/AA criteria. RESULTS: The GPCOG-C had the sensitivity of 62.3% and specificity of 84.6% in screening MCI, which had comparable efficacy as the NIA/AA criteria. In screening dementia, GPCOG-C had a lower sensitivity (63.7%) than the MMSE and a higher specificity (82.6%) higher than the MMSE. CONCLUSIONS: The GPCOG-C is a useful and efficient tool to identify dementia and MCI in older Chinese in outpatient clinical settings.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".