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 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".