Efficacy studies of hyperbaric oxygen and donepezil hydrochloride joint intervention for mild vascular cognitive impairment
Bibliographic record
Abstract
Objective To explore the clinical effects with hyperbaric oxygen and hydrochloric donepezil combination therapy.For mild vascular cognitive impairment.Methods A total of 180 cases with mild vascular cognitive impairment were selected in our hospital from July 2011 to August 2012 and divided into four groups,each group had 45 cases,group Ⅰ were treated with hyperbaric oxygen treatment,group Ⅱ were treated with donepezil hydrochloride treatment,group Ⅲ were treated with hyperbaric oxygen combined with donepezil hydrochloride,group Ⅳ were treated with ginkgo biloba treatment.We compared MOCA ratings of the four groups of patients before and after treatment and before and after treatment MOCA score difference.Results Compared with before treatment MOCA score in the After treatment,Ⅰ,Ⅱ,Ⅲ,Ⅳ group were significantly higher,and there were significant differences(P 0.05).Group Ⅲ patients MOCA score difference was significantly higher than Ⅰ,Ⅱ and Ⅳ patients(P 0.05);group Ⅰ,Ⅱ patients MOCA core difference was significantly higher than that of the group Ⅳ(P 0.05);group Ⅱ patients MOCA score difference was slightly higher than the group Ⅰ,there were not have significant difference between the two groups(P 0.05).Conclusion Mild vascular cognitive impairment in patients with hyperbaric oxygen and hydrochloric acid donepezil combination therapy to the patient's symptoms are significantly improved and delay.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".