Influences on subjective happiness feeling in patients with cerebral infarction and anxiety treated by integrated traditional Chinese and Western medicine
Bibliographic record
Abstract
[Objective] To explore the influence of integrated Chinese and Western medicine on anxiety degree and subjective happiness feeling in patients with cerebral infarction associated with anxiety.[Methods] Trait anxiety inventory(TAI) and Memorial University of Newfoundland Scale of Happiness(MUNSH) were used to evaluate the condition of 60 patients with cerebral infarction associated with anxiety.The patients participated in the study were randomly divided into Western medicine group and Combined traditional Chinese and Western medicine group with 30 cases in each group.8 weeks after treatment,TAI score and MUNSH score were tested.[Results] The TAI score of patients in two group was obviously decreased with a statistically significant difference(P0.05).The MUNSH score was obviously increased in the combined traditional Chinese and Western medicine group.The result of comparison between the two groups showed that the MUNSH score of patients in the combined traditional Chinese and Western medicine group were more obviously improved compared with patients in the Western medicine group only,and the difference was statistically significant(P0.05).[Conclusion] The treatment of integrated traditional Chinese and Western medicine can obviously release anxiety and enhance subjective happiness feeling in patients with cerebral infarction associated with anxiety.
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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 | 0.001 |
| 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".