Preliminary study on computer aided interactive classified software for evaluating learning and memory in patients with Parkinson's disease
Bibliographic record
Abstract
Objective To study the efficacy of compute-aided interactive classified software for evaluating learning and memory in patients of Parkinson's disease(PD).Methods Fifteen normal controls and 22 PD patients divided into two groups based on medicine usage: PD(on)(n=15) and PD(off)(n=7) were enrolled in this study.Each group was compared with others by the unified PD rating scale(UPDRS scores),cognitive status(mini-mental state examination,MMSE and Montreal cognitive assessment,MoCA),depression state,and the computer aided interactive classified learning task.Results ①Scores of MMSE and MoCA in PD(on) and PD(off) groups showed no significant difference with that of normal controls(P0.05);②Task Ⅰ(feedback-based classified task): PD(on) group showed significant difference when compared with PD(off) group(P0.05);③TaskⅡ(feedback-based probabilistic classification): in reward task,PD(off) showed significant reduction(P0.05) while PD(on) group did not;in the punish task,there were no significant differences between normal control and PD(on) and PD(off) group(P0.05).Conclusions PD(on) group showed reduction in feedback-based classified task,while PD(off) group showed reduction in reward feedback task,which further indicated that in PD patients the shortage of dopamine in dorsal striatum was more prominent than that in ventral striatum,and the use of drugs might affect the results in feedback-based learning.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".