Comparison of vascular cognitive impairment - no dementia by multiple classification methods
Bibliographic record
Abstract
AIMS: To compare vascular cognitive impairment-no dementia (VCI-ND) using different classification methods. METHODS: We recruited 56 patients with VCI-ND between April 2012 and March 2013. We used a battery of neuropsychological tests to classify patients with VCI-ND into different subtypes based on memory and executive function as follows: cognitive screening (Mini-Mental State Examination, MMSE), memory (Auditory Verbal Learning Test, AVLT), executive/attention (Shape Trails Test, STT; Stroop Color-Word Test, SCWT; Reading the Mind in the Eyes, RME; Digit Ordering Test-A, DOT-A; Symbol Digit Modalities Test, SDMT), language (Action Naming Test, ANT; Boston Naming Test, BNT; Famous Face Test, FFT; Similarity Test, ST; Verbal Fluency Test, VFT) and visuospatial function (Complex Figure Test, CFT). RESULTS: The two groups had comparable demographic information (P>0.05). Amnestic VCI-ND (aVCI-ND) patients obtained significantly lower scores compared with individuals with nonamnestic VCI-ND (naVCI-ND) on the AVLT memory test, VFT language test and VFT-alternating executive test (P<0.05). Additionally, executive VCI-ND (eVCI-ND) patients performed significantly longer than nonexecutive VCI-ND (neVCI-ND) patients on the SCWT-C timed executive test. Finally, eVCI-ND patients obtained significantly lower scores compared with neVCI-ND patients on the RME, DOT-A and SDMT-correct executive tests and the ANT, BNT and ST language tests (P<0.05). CONCLUSION: aVCI-ND patients performed poorly compared with naVCI-ND patients in terms of executive and language functions, while eVCI-ND patients performed poorly compared with neVCI-ND patients in terms of language function.
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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".