Exploring the Impact of Function-Based Affolter Therapy in Conjunction With Scalp Acupuncture on Poststroke Cognitive Impairment
Bibliographic record
Abstract
Objective This study aims to assess the effects of function-based Affolter therapy in conjunction with scalp acupuncture on cognitive impairment following a stroke. Methods A total of 100 patients with poststroke cognitive impairment were systematically selected and subsequently randomized into five distinct groups, each comprising 20 participants. Group A underwent Affolter therapy, Group B received scalp acupuncture, Group C was subjected to a nonsynchronous combination of both treatments, Group D received a synchronous combination of both therapies, and Group E was designated as the control group. Pre- and posttreatment assessments of cognitive function were conducted using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Test (MoCA). Activities of daily living (ADL) were evaluated using the ADL scale, while the integrity of white matter nerve fibers in the hippocampus was observed through diffusion tensor imaging, and the resultant data were subjected to statistical comparison. Results Multiple comparisons were conducted between pre- and posttreatment MMSE scores, and noteworthy distinctions emerged, specifically between Groups C and A, B, D, and E ( p < 0.05), as well as between Groups D and A, B, and E ( p < 0.05). A statistically significant variance in the MoCA test results pre- and posttreatment was observed between Groups D and A, B, C, and E ( p < 0.05). The assessment of ADL yielded significant differences, notably between Groups B and A, C, and D ( p < 0.05); Groups C and A, B, D, and E ( p < 0.05); and Groups D and A, B, C, and E ( p < 0.05). Furthermore, the changes in diffusion tensor imaging-derived apparent diffusion coefficient values pre- versus posttreatment demonstrated statistically significant differences between Groups D and A, B, C, and E ( p < 0.05). Conclusion The effectiveness of treating poststroke cognitive impairment in patients is notably superior when using a combination of function-based Affolter therapy and synchronous scalp acupuncture when compared with alternative therapeutic approaches.
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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".