Clinical effects of oxiracetam for treatment of vascular cognitive impairment in varying degree
Bibliographic record
Abstract
Objective:To investigate the effects of oxiracetam treatment for vascular cognitive impairment(VCI) in varying degrees.Methods:Sixty patients with VCI were selected.Among them,30 patients were with vascular cognitive impairment no dementia(VCIND)and 30 patients with vascular dementia(VD).All patients received conventional base treatment and the oxiracetam treatment.The event-related potentials P300(ERP-P300) and Montreal cognitive assessment(MoCA) of all patients were observed and compared before and after the treatment.Results:After the treatment,the scores of MoCA of all patients showed improvement(VCIND:t = 11.886,P 0.01;VD:t = 2.242,P 0.05).After the treatment,ERP-P300 of the VCIND group was improved significantly,the latency period of ERP-P300(P300-PL) was significantly shortened(t = 5.799,P 0.01) while the amplitude(P300Amp) was significantly increased(t = 17.603,P 0.01);but the ERP-P300 of the VD group had no obvious improvement(PL:t = 1.414,P 0.05;Amp:t = 1.069,P 0.05).There were significant differences in the scores of MoCA,the latency period and amplitude of ERP-P300 between VCIND group and VaD group(t = 2.135,4.402,7.682,P 0.05).Conclusion:Oxiracetam are beneficial in the treatment of VCI,especially effective to the patients with VCIND.
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 | 0.000 | 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) | 0.001 | 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".