Comparison of the diagnostic accuracy of the Cognitive Performance Scale (Minimum Data Set) and the Mini‐Mental State Exam for the detection of cognitive impairment in nursing home residents
Bibliographic record
Abstract
OBJECTIVE: To compare the diagnostic accuracy of an outcome measurement scale of the Minimum Data Set of the Resident Assessment Instrument for nursing homes (MDS/RAI-NH), the Cognitive Performance Scale (CPS) and the Mini-Mental State Exam (MMSE) for the detection of cognitive impairment. The Cambridge Examination for Mental Disorders of the Elderly--Revised (CAMDEX-R) was used as the reference standard. STUDY DESIGN AND SETTING: This study was part of a larger prospective study (QUALIDEM) involving a diagnostic procedure and two-year follow-up on the quality of primary care for demented patients. CAMDEX-R and MDS/RAI-NH were administered to 198 residents, aged 65 or more, living in 42 low and high care institutions for aged people. MAIN OUTCOME MEASURES: Indicators of diagnostic accuracy: sensitivity, specificity, predictive values, likelihood ratios, odds ratio and area under receiver operating characteristics curve (AUC). RESULTS: The CAMDEX-based prevalence of cognitive impairment was 75%. The diagnostic values of a CPS score of two or more for the detection of cognitive impairment were: sensitivity = 0.81; specificity = 0.80; PPV = 0.92; NPV = 0.57. The diagnostic values of a MMSE score of less than or equal 23 were: sensitivity = 0.97; specificity = 0.59; PPV = 0.88; NPV = 0.85. For CPS, the area under the receiver operating characteristic (ROC) curve was 0.87 (95% CI, 0.81-0.91), and not significantly different (p = 0.63) from the MMSE score, 0.88 (0.83-0.93). CONCLUSIONS: CPS and MMSE demonstrated similar performance to detect cognitive impairment in nursing home residents.
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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 | 0.012 | 0.058 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".