Depression as a predictor of disease progression and mortality in cancer patients
Bibliographic record
Abstract
BACKGROUND: Cancer patients and oncologists believe that psychological variables influence the course of cancer, but the evidence remains inconclusive. This meta-analysis assessed the extent to which depressive symptoms and major depressive disorder predict disease progression and mortality in cancer patients. METHODS: Using the MEDLINE, PsycINFO, CINAHL, and EMBASE online databases, the authors identified prospective studies that examined the association between depressive symptoms or major/minor depression and risk of disease progression or mortality in cancer patients. Two raters independently extracted effect sizes using a random effects model. RESULTS: Based on 3 available studies, depressive symptoms were not shown to significantly predict cancer progression (risk ratio [RR] unadjusted = 1.23; 95% confidence interval [CI], 0.85-1.77; P = .28). Based on data from 25 independent studies, mortality rates were up to 25% higher in patients experiencing depressive symptoms (RR unadjusted = 1.25; 95% CI, 1.12-1.40; P < .001), and up to 39% higher in patients diagnosed with major or minor depression (RR unadjusted = 1.39; 95% CI, 1.10-1.89; P = .03). In support of a causal interpretation of results, there was no evidence that adjusting for known clinical prognostic factors diminished the effect of depression on mortality in cancer patients. CONCLUSIONS: This meta-analysis presented reasonable evidence that depression predicts mortality, but not progression, in cancer patients. The associated risk was statistically significant but relatively small. The effect of depression remains after adjustment for clinical prognosticators, suggesting that depression may play a causal role. Recommendations were made for future research to more clearly examine the effect of depression on cancer outcomes.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.019 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".