Treatment of depressive symptoms in patients with breast cancer: A randomized controlled trial comparing cognitive therapy and bright light therapy.
Bibliographic record
Abstract
OBJECTIVE: This randomized controlled trial (RCT), conducted in patients with breast cancer, aimed to compare the effects of cognitive therapy (CT), bright light therapy (BLT), and a waiting-list control condition (WLC) on depressive symptoms. METHOD: Sixty-two women were randomly assigned to an 8-week CT (n = 25), BLT (n = 26), or WLC (n = 11). Participants completed the depression subscale of the Hospital Anxiety and Depression Scale (HADS-D), the Beck Depression Inventory-II (BDI-II), and the Hamilton Depression Rating Scale (HDRS) at pre- and posttreatment (and postwaiting for WLC), as well as 3 and 6 months later. RESULTS: At posttreatment, CT patients had a significantly greater reduction of depressive symptoms than WLC on the HADS-D and the BDI-II. BLT patients had a greater reduction of depressive symptoms than WLC on the HADS-D only. After WLC participants were reassigned to CT or BLT, a superiority of CT over BLT was found on the BDI-II at posttreatment. Patients of both active conditions showed a good sustainment of treatment gains at follow-ups. CONCLUSIONS: Although replication with larger samples is needed, these results confirm the efficacy of CT for depression in the context of breast cancer and suggest that BLT could be of some utility when CT is not available or desired. (PsycINFO Database Record
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".