Randomized clinical trial on cognitive therapy for depression in women with metastatic breast cancer: Psychological and immunological effects
Bibliographic record
Abstract
OBJECTIVE: Depression is particularly prevalent in patients with advanced cancer. Cognitive therapy (CT) is an empirically supported treatment for depression in the general population. However, efficacy remains to be demonstrated in patients with advanced cancer. A prior controlled trial of CT in a group format showed improvements in depression, mood disturbance, and self-esteem; however, these effects were not maintained over time. Studies examining the efficacy of individual format CT interventions that may ensure more long-term maintenance of benefits are necessary. This study assessed the efficacy of CT for depression administered individually in women with metastatic breast cancer and its effect on immune function. METHOD: Forty-five women were randomly assigned to either individual CT or to a waiting-list control (WLC) condition. CT was composed of eight weekly sessions of CT and three booster sessions administered at 3-week intervals following the end of treatment. RESULTS: Patients treated with CT had significantly lower scores on the Hamilton Depression Rating Scale at posttreatment compared to untreated patients. Pooled data from both groups indicated significant reductions of depressive symptoms from pre- to posttreatment, as well as reduction of associated symptoms including anxiety, fatigue, and insomnia symptoms. These effects were well sustained at the 3- and 6-month follow-up evaluations. CT for depression did not appear to have a significant impact on immune functioning. SIGNIFICANCE OF RESULTS: Findings of this study support the efficacy of CT for depression in this population and suggest that the administration of individual and booster sessions after treatment termination may be instrumental in sustaining the treatment effects over time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".