Evaluation of the NINDS-CSN 5-minutes protocol as a cognitive screening test to detect Parkinson’s disease dementia: a study of a Brazilian sample
Bibliographic record
Abstract
Introduction: Many cognitive screening tests have been investigated for the diagnosis of Parkinson’s disease dementia (PDD), due to its prevalence and its interference in the evolution of the disease, quality of life and in the treatment response of the patients with Parkinson’s disease (PD). Therefore, more effective and faster cognitive screening tests are needed. Objectives: To evaluate the usefulness of the NINDS-CSN 5-minutes protocol assessment (NC5MPA) in PD patients as screening test for the detection of PDD, as well as to test if the association with the cube drawing test (CDT) can increase the test accuracy. Methods: A total of 98 patients with PD were evaluated using the NC5MPA, combined with the CDT, Mini Mental State Examination and the Montreal Cognitive Assessment (MoCA). These patients were also evaluated for mild cognitive impairment (MCI) and dementia by the Clinical Dementia Rating Scale (CDR). Results: There was a good correlation (with P value < 0.00) between the test scores and PDD, but the results of the 3 tests for MCI was > 0.05. The NC5MPA test has had sensitivity of 78.5%, specificity of 85.7%, accuracy of 82.6%, positive predictive value of 80.4% and negative predictive value of 84.2%, in addition to demonstrate an average performance time of 3.2 minutes (3.08–3.31). The association with the CDT has led to a little significant increase in sensitivity and has showed a decrease in specificity and accuracy, besides increase the test performance time. In assessing the interference of education level, the results were influenced by the small sample size of the 5 to 8 years of education group. Conclusion: The NC5MPA test has proven up to be a good screening test for PDD, being even faster and easier to perform, but more tests with larger populations are necessary to assess the accuracy of this test for MCI and to assess if there is interference of education level in the test accuracy.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".