The impact of anxiety and depression on hematologic malignancy outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hematologic malignancies (HMs) are associated with high morbidity and mortality. Depression and anxiety, prevalent in patients with HM, may adversely impact survival, but their prognostic role remains unclear. MATERIALS AND METHODS: This systematic review and meta-analysis followed PRISMA guidelines and was registered in PROSPERO (CRD42024568789). We searched PubMed, SCOPUS, and Web of Science for studies examining the association between depression or anxiety and survival outcomes in patients with HMs. Data extraction and quality assessment (using the Newcastle-Ottawa Scale and RoB2) were conducted independently by multiple reviewers. Random-effects meta-analyses were performed, with subgroup, sensitivity, and publication bias analyses. Additionally, meta-regression analyses were used to explore the impact of study-level factors on the observed associations. RESULTS: Twenty-nine studies (31 cohorts) comprising 419,054 patients with various HMs were included. Depression was significantly associated with poorer OS (HR = 1.17, 95% CI: 1.09-1.27), while anxiety showed a non-significant association (HR = 1.20, 95% CI: 1.00-1.44). Depression was not significantly associated with EFS or CSS, however, anxiety was linked to poor EFS. Publication bias was detected, and adjustment attenuated the associations. Subgroup and sensitivity analyses confirmed the robustness of the main findings. Meta-regression indicated that heterogeneity in effect sizes was partially explained by sample size. CONCLUSIONS: Depression is associated with reduced overall survival in patients with HMs, underscoring the importance of psychological assessment and early intervention in this population. Further research is needed to clarify the impact of anxiety and to inform targeted supportive care strategies.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".