Application of the modified qualitative scoring of MMSE pentagon test in the differential diagnosis of lewy body dementia and Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background To explore the value of the modified qualitative scoring of MMSE pentagon test (mQSPT) in the differential diagnosis of Lewy body dementia (DLB) and Alzheimer’s disease (AD). Method Study the patients who met the inclusion criteria in the Department of Neurology, Xuanwu Hospital, Capital Medical University from January 2018 to August 2021. The baseline data of gender, age and education, Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment Scale (MoCA) and Clinical Dementia Assessment Scale (CDR) scores of 61 DLB patients and 71 AD patients were analyzed retrospectively. At the same time, the scores of sub items in the scale that can reflect visualspatial impairment were recorded,: i.e. connection test, cube copy, clock drawing test in MoCA, and pentagon copy test in MMSE. The images in MMSE pentagon copy test of these patients were further scored according to QSPT and mQSPT, and compared between the two groups. Multivariate stepwise logistic regression was used to analyze the differential efficacy of QSPT, mQSPT combined with other clinical psychological evaluation between DLB and AD. Result There were significant differences between DLB and AD patients in connection test, cube copy, clock drawing test, pentagon copy test, QSPT, mQSPT and gender, but there were no significant differences in MMSE, MoCA, age and education. The sensitivity of QSPT in differentiating DLB and AD was 71.8%, the specificity was 67.2%, the area under ROC curve was 0.682 (95%CI: 0.584‐0.772), and the cut‐off value was 9.5. The sensitivity of mQSPT in differentiating DLB and AD was 68.9%, the specificity was 84.5%, the area under ROC curve was 0.78 (95%CI: 0.696‐0.862), and the cut‐off value was 8.5. Multivariate stepwise logistic regression showed that while QSPT failed to establish any suitable modeling, mQSPT and cube copy were related to the distinction between the two diseases. Moreover, the efficacy of these two indexes to distinguish AD and DLB: the sensitivity was 70.5%, and the specificity was 84.1% and the area under the ROC curve was 0.817(95%CI: 0.743‐0.891). Conclusion The mQSPT can be used for the screening tool in differential diagnosis between AD and DLB patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".