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Effects of ST. John's wort on depressive disorder in elderly patients with hypertension and diabetes

2009· article· en· W3029110366 on OpenAlexaboutno aff
Xiaojun Ren, Hui Li, Jingqiu Cui, Hongmei Li, Yu Wang, Jingyan Li, Jin-quan Liu

Bibliographic record

VenueIntern J Endocrinol Metab · 2009
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsnot available
Fundersnot available
KeywordsHamdMedicineBlood pressureDiabetes mellitusPlaceboMajor depressive disorderInternal medicinePlacebo groupDepressive symptomsDepression (economics)Significant differenceEndocrinology

Abstract

fetched live from OpenAlex

Objective To explore the clinical curative effect of ST. John's wort(SWE) on depressive disorder in elderly patients with hypertension and diabetes. Methods 106 hypertensive patients with diabe-tes and depressive disorder were randomly assigned to SWE group (n = 30) ,psychotherapy group (n = 24) , physicotherapy group (n =28) and placebo group (n = 26) for 12 weeks' treatment. The clinical effect were evaluated separately with reduction percent of HAMD and after the treatment, blood pressure levels and HbA1c were monitored. Results (1) The reduction percent of HAMD was highest(80%) in SWE group, lower in physieotherapy and psychotherapy group (50% and 54.2%), well lowest in placebo group(37%), the difference was significant(P < 0.05). The effect of SWE was better than that of the others (P < 0.05). (2) The level of blood pressure in SWE was significant lower than that of the others(P < 0.05). (3) The lev-el of HbA1c in SWE group was significant lower than that of the others(P < 0.05). Conclusion SWE can efficiently improve the depressive disorder in elderly patients with hypertension and diabetes, and it is good for controlling blood pressure and HbA1c of the patients. Key words: Hypertension;  Diabetes mellitus;  Depressive disorder;  Psychotherapy;  Physicotherapy

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.248
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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