Diagnostic Performance of Short Portable Mental Status Questionnaire for Screening Dementia Among Patients Attending Cognitive Assessment Clinics in Singapore
Bibliographic record
Abstract
INTRODUCTION: The Short Portable Mental Status Questionnaire (SPMSQ) is a brief cognitive screening instrument, which is easy to use by a healthcare worker with little training. However, the validity of this instrument has not been established in Singapore. Thus, the primary aim of this study was to determine the diagnostic performance of SPMSQ for screening dementia among patients attending outpatient cognitive assessment clinics and to assess whether the appropriate cut-off score varies by patient's age and education. A secondary aim of the study was to map the SPMSQ scores with Mini-Mental State Examination (MMSE) scores. MATERIALS AND METHODS: SPMSQ and MMSE were administered by a trained interviewer to 127 patients visiting outpatient cognitive assessment clinics at the Singapore General Hospital, Changi General Hospital and Tan Tock Seng Hospital. The geriatricians at these clinics then diagnosed these patients with dementia or no dementia (reference standard). Sensitivity and specificity of SPMSQ with different cut-off points (number of errors) were calculated and compared to the reference standard using the Receiver Operator Characteristic (ROC) analysis. Correlation coefficient was also calculated between MMSE and SPMSQ scores. RESULTS: Based on the ROC analysis and a balance of sensitivity and specificity, the appropriate cut-off for SPMSQ was found to be 5 or more errors (sensitivity 78%, specificity 75%). The cut-off varied by education, but not by patient's age. There was a high correlation between SPMSQ and MMSE scores (r = 0.814, P <0.0001). CONCLUSION: Despite the advantage of being a brief screening instrument for dementia, the use of SPMSQ is limited by its low sensitivity and specificity, especially among patients with less than 6 years of education.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".