Evaluation of scenario-based storytelling therapyas an intervention for cognitive impairment after ischemic stroke
Bibliographic record
Abstract
Objective To explore and evaluate the scenario-based storytelling therapy as an intervention on cognitive impairment after ischemic stroke. Methods Totally 64 patients with cognitive impairment after acute ischemic stroke were randomly divided into control group (n=31, and 1 case lost to follow-up) and intervention group (n=32 ) , the patients in the two groups were treated with basic treatment such as hypertension management, blood lipid regulation and healthy lifestyle intervention. The control group was given routine nursing and routine cognitive function training, and the intervention group was scenario-based storytelling therapy. The Mini-Mental State Examination (MMSE), The Montreal Cognitive Assessment (MoCA) Hamilton Anxiety Rating Scale(HAM-A) score and the effective rate of intervention were compared between the two groups. Results The effective rate of the intervention group was 87. 50%(28/32) , which was higher than35. 48%(11/31) in the control group(P＜0. 05). After intervention, scores of MMSE and MoCA increased and HAM-A score decreased in two groups, and patients in the intervention group achieved better improvement in MMSE, MoCA and HAM-A compared with those in the control group(P＜0. 05). Conclusion The scenario-based storytelling therapy is potentially effective to improve the cognitive function of patients with cognitive impairment after ischemic stroke and relieve their anxiety status. (目的 探讨并评价情境故事疗法对于缺血性卒中后认知功能损害的干预效果。方法 选取64例急性缺血性脑卒中后认知功能损害患者随机分为对照组31例(失访1例)和干预组32例。对照组给予常规干预, 干预组在对照组基础上基于情境故事疗法进行认知功能干预。观察2组干预后的临床疗效, 比较2组干预前后简明精神状态检查(MMSE)量表、蒙特利尔认知评估量表(MoCA)和汉密尔顿焦虑量表评分。结果 干预组总有效率87. 50%(28/32)高于对照组的35. 48%(11/31), 差异有统计学意义(P＜0. 05)。干预后2组MMSE、MoCA评分升高, 汉密尔顿焦虑量表评分降低, 且干预组评分优于对照组, 差异有统计学意义(P＜0. 05)。结论 实施情境故事疗法有利于改善缺血性卒中后认知功能损害患者的认知能力, 缓解焦虑程度, 值得推广。)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 teacher head, 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".