Controlled clinical research on cognitive function of long-term bed patients improved by functional electrical stimulation
Bibliographic record
Abstract
Objective The study and explore the clinical effect of functional electrical stimulation (FES) for patients with cognitive function declined due to long-term bed.Methods Sixty patients with cognitive function declined,were randomly divided into two groups:the control group and the FES treatment group.Two groups were all treated by conventional treatment way,on this basis,the FES group was treated by functional electrical stimulation extra,but not in control group.Two groups before and after treatment,the Montreal cognitive assessment (MOCA) score and auditory event related potentials P300 were tested respectively,to assess the clinical efficacy.Results There was no statistical significant difference in results of MOCA and P300 test before treatment (P ＞ 0.05).After 3 weeks,the MOCA of FES group were higher than those in the control group (P ＜ 0.05),the results of P300 test of FES group were obviously lower than those of control group,especially in P2,P3 latency and P2 amplitude (P ＜ 0.05).Conclusion The therapeutic method of FES can not only improve patients' scores of MOCA,but also decrease the results of P300 test,which can improve the cognitive function of those patients. Key words: Functional electrical stimulation; Cognitive function; Rehabilitation
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".