Effect of St. John's wort extract on depressive disorder in elderly patients with unstable angina.
Bibliographic record
Abstract
BACKGROUND: The elderly patients with coronary heart disease (CHD) are often accompanied with depression. This study aimed to assess the effect of St. John's wort extract (SWE) on depressive disorder in elderly patients with unstable angina pectoris. METHODS: Altogether 170 patients who met the set criteria were enrolled in this prospective study. They were randomly divided into SWE group (44 patients), Deanxit group (44), psychotherapy group (42), and control group (40). The effectiveness of SWE was evaluated by reduced percentage of Hamilton depression (HAMD) scale and reduced frequency of angina pectoris attack, which were measured before and at 12 weeks after the treatment with SWE. RESULTS: The reduced percentages of HAMD scale were 79.5%, 56.8% and 57.1% in the SWE, Deanxit and psychotherapy groups, respectively. Compared with the control, the three groups had significant differences in the percentages (P<0.001). The improvement after the treatment was more significant in the SWE group than in the Deanxit or psychotherapy group (P<0.05). The improvement of angina pectoris evaluated by the Canadian Cardiac Society Classification was significantly better in the treatment groups (88. 7%, 65. 9%, 57.1%) than in the control group, and it was marked in the SWE group (P<0.001). Angina pectoris attack, its frequencies, durations and electrocardiographic changes were significantly improved in the treatment groups than in the control group (F=6.05, 4.58, 5.12, P<0.01). They are markedly improved in the SWE group (P<0.05). CONCLUSION: SWE can improve depressive symptoms more significantly in elderly patients with unstable angina pectoris than Deanxit or psychotherapy, proving that SWE contributes to better treatment of angina attack as well.
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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".