Application of mindfulness-based stress reduction plan in post-stroke patients with mild depression
Bibliographic record
Abstract
Objective To explore the therapeutic effect of mindfulness-based stress reduction in patients with post-stroke mild depression. Methods A total of 80 patients with mild depression after stroke received by our hospital from January 2023 to December 2023 were selected as the study subjects. Randomly divide the patients into two groups: 40 in the control group and 40 in the combination group. The control group received conventional intervention therapy, while the combination group was treated with a mindfulness-based stress reduction plan combined with conventional intervention therapy. Patients in both groups were treated for 8 weeks. The general information of patients in the two groups was statistically analyzed. Self-rating Depression Scale (SDS), Hamilton Depression Rating Scale (HAMD-17), Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) scores and inflammatory factors (including TNF-α, hs-CRP, IL-6) expression levels were used to evaluate the efficacy of patients before and after treatment, and the total effective rate was counted for the two groups. Results After treatment, compared with that before treatment, the HAMD-17 and SDS scores and inflammatory factor levels of patients in the control group and combination group were significantly decreased (all p < 0.05), while the MMSE and MoCA scores of patients in the control group and combination group were significantly increased (all p < 0.05). However, after treatment, the HAMD-17 and SDS scores and inflammatory factor levels of patients in the combination group were significantly lower than those in the control group (all p < 0.05). The scores of MMSE and MoCA were significantly higher than those in the control group (p < 0.05). Conclusion In the treatment of stroke patients with mild depression, mindfulness-based stress reduction combined with conventional intervention therapy can improve the overall response rate compared with conventional intervention therapy alone and has a significant advantage in reducing depression.
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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) | 0.000 | 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".