Application of Patient Version of General Practitioner Assessment of Cognition for Screening Elderly with Mild Cognitive Impairment in Community
Bibliographic record
Abstract
<bold>Objective</bold> To explore the application value of the patient version of general practitioner assessment of cognition (P-GPCOG) for screening elderly with mild cognitive impairment (MCI) in community. <bold>Methods</bold> A total of 905 elderly were recruited from West China Tianfu Hospital, Sichuan University and Chongzhou Second Hospital from April 2022 to November 2023, including 627 cases in normal cognitive function group (NC group) and 278 cases in MCI group. The cognitive function of the subjects was evaluated by P-GPCOG, the Montreal Cognitive Assessment (MoCA), and the 8-item ascertain dementia (AD8). The independence of daily living ability was evaluated by the Lawton Instrumental Activities of Daily Living Scale (IADLs). The Geriatric Depression Scale-15 (GDS-15) was used to assess the depressive symptoms of the subjects. The receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value were calculated to evaluate the application value of GPCOG screening. <bold>Results</bold> Compared with the NC group, the MoCA and Lawton-IADL scores were significantly lower in the MCI group (<italic>P</italic><0.001, <italic>P</italic>=0.022). The AD8 and GDS-15 scores were significantly higher (<italic>P</italic><0.001). There was a significant difference in the P-GPCOG scores between the two groups (<italic>Z</italic>=15.001, <italic>P</italic><0.001). Compared with the NC group, correct rate of the time orientation, clock drawing, reporting events and delayed recall, and the total score were significantly lower in the MCI group, with statistically significant differences (<italic>P</italic><0.001). In the MCI group, the order of cognitive impairment rate from high to low was delayed recall test, clock drawing test, time orientation and reporting events. AUC of P-GPCOG for differentiating MCI patients was 0.804 [95% <italic>CI</italic> (0.773, 0.836), <italic>P</italic><0.001], with an optimal cutoff value of 7/8 points. The sensitivity was 82.7%, the specificity was 72.6%, the positive predictive value was 57.2%, and the negative predictive value was 90.4%. <bold>Conclusion</bold> The P-GPCOG is a good community-based cognitive screening tool for screening MCI patients in community, but its original scoring criteria needs to be refined to improve the correct screening rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".