Effective maNagement of depression among patients witH cANCEr (ENHANCE): A hybrid systematic review and (attempted) network meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Optimal intervention(s) for depression among people with cancer are unknown, as all available approaches have not been compared. This hybrid systematic review aimed to identify the most effective and acceptable intervention(s) using network meta-analysis (NMA). METHODS: Randomized controlled trials (RCTs) of depression interventions among adults with cancer experiencing depressive symptoms were identified from database searches for previous systematic reviews and more recent RCTs. Screening, data extraction, Risk of Bias (RoB2) and Research Integrity Assessment (RIA; for descriptive rather than screening purposes) were performed independently, in duplicate. Primary outcomes were change in depressive symptoms (efficacy/effectiveness) and the rate who discontinued (acceptability). As the planned NMA was not appropriate, a narrative critical synthesis was performed. FINDINGS: 70 RCTs (6831 participants) were included (43 psychotherapy, 14 pharmacotherapy, 8 complementary and alternative medicine, 7 collaborative care, 4 exercise, and 3 combination therapy interventions). No significant differences regarding acceptability were evident. Reliable efficacy/effectiveness comparisons using NMA were not possible due to RoB (44.3 % Some concerns, 54.3 % High RoB). Only 10 RCTs had no integrity concerns. Integrity issues included no pre-registration (n = 56/80 %), insufficient reporting on randomisation (n = 27/38.6 %) and ethics (n = 32/40 %), and questionable effect sizes (n = 26/37 %). The most reliable evidence was for collaborative care. CONCLUSIONS: The literature on depression interventions for people with cancer is at RoB, pointing to an urgent need for high-quality research. Until such evidence is available, treatment decisions should continue to be based on evidence from other patient groups and clinical expertise, though there is some evidence that collaborative care is effective. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021290145 https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=290145.
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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 | 0.082 | 0.158 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".