Clinical study of cognitive behavior therapy combined with venlafaxine in the treatment of somatoform pain disorder
Bibliographic record
Abstract
Objective To explore efficacy of cognitive behavior therapy combined with venlafaxine in the treatment of the somatoform pain disorder. Methods All 60 patients with somatoform pain disorder were randomly assigned to study groups (treated with cognitive behavior therapy combined with venlafaxine ) and control group (treated with venlafaxine alone) for 8 weeks. Each had 30 cases. Before the treatment and on the 2, 4 and 8 weekend after the treatment, Simple McGill pain scale (SF-MPQ), Hamilton Depression Scale(HAMD) and Treatment Emergent Side effect Scale(TESS) were used to evaluate the clinical efficacy(degree of pain reducing), degree of depression and adverse effect respectively. Results After 8 weeks, the efficacy in the study group (66.67%) was significantly higher than that in the control group (40.00%)(χ2=4.29,P0.05). The scores of HAMD, total and each factor of SF-MPQ in both groups were lower significantly after treatment than those before treatment. So as the scores in the study group than those in the control group after 4 and 8 weeks (t = 3.04,3.87,2.99,2.90,8.25,2.91,5.18,4.65,6.90,4.67,P 0.05). The incidence of adverse effect had no significant difference between the two groups (χ2=0.08,P0.05). Conclusions Cognitive therapy combined with venlafaxine is more effective than venlafaxine alone in the treatment of the somatoform pain disorder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".