Efficacy of Computer aided Training combined with Hyperbaric Oxygen Therapy on Stroke Patients with Cognitive Dysfunction
Bibliographic record
Abstract
Objective: To explore the clinical effects of computer aided training combined with hyperbaric oxygen therapy(HBOT) on stroke patients with cognitive dysfunction. Methods: Sixty stroke patients with cognitive dysfunction were randomly assigned to groups of control, HBOT and combined treatment, with 20 cases in each group. The 3 groups were treated with basic medication and traditional rehabilitation therapy while HBOT group was treated additionally with HBOT and combined treatment group was treated additionally with HBOT and computer aided training. Montreal Cognitive Assessment(Mo CA), Fugl-meyer Scale(FMA) and Barthel Index(BI)were assessed at pre-therapy and 4 weeks after treatment. Results: The scores of Mo CA, FMA and BI in HBOT group and combined treatment group after treatment were much higher than those of control group(P0.05), while the scores of Mo CA and BI in combined treatment group demonstrated much higher than those of HBOT group(P0.05). However, there was no significant difference of FMA scores between 2 groups after treatment. The correlation between sub-items of Mo CA and FMA, BI were analyzed. There were significant correlations between FMA scores and five sub-items(VS-EF, MEM, ATT, ABS, D-MEM) scores of Mo CA(r=0.324~0.521,P0.05 or 0.01). Significant correlations between BI scores and seven sub-items(VS-EF, NAM, MEM, ATT, ABS, D-MEM,ORI) scores of Mo CA(r=0.342~0.537,P0.05 or 0.01) were also shown. Conclusion: Computer aided training combined with HBOT can effectively improve cognitive function of stroke patients.
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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 | 0.000 |
| 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".