Are cancer pain and depression interdependent? A systematic review
Bibliographic record
Abstract
OBJECTIVE: Pain and depression are common in cancer patients. As these are both highly prevalent, the issue of a possible interdependent association has been raised. The aim of this systematic review was to examine the available literature and explore whether there is any evidence to support a causal relationship between cancer pain and depression. METHODS: An extensive literature search was undertaken. The following databases were searched electronically: Medline (1950-2007), Embase (1988-2007), CINAHL (1982-2007) and the Cochrane Database of Systematic Reviews (Issue 2 2007). The initial literature search revealed 892 articles. Following initial screening 41 articles were independently reviewed in detail. Fourteen articles were eligible for inclusion. RESULTS: The mean prevalence of both depression and pain was 36.5% (range 22.1-49.0). In 9 out of 14 studies a statistically significant association was demonstrated between pain and depression. Pain intensity positively correlated with depression (P<0.05). Items such as 'worst pain' and 'enjoyment of life' (on the Brief Pain Inventory) correlated significantly with depression. When using the McGill Pain Questionnaire, depressed patients used more affective pain descriptors. It was also shown that the longer the duration of pain, the higher the risk of depression. CONCLUSIONS: Pain and depression are highly prevalent in cancer patients; however, there have been no appropriately designed studies to examine a causal relationship. Although associations exist, the evidence available is not sufficient to support an interdependent relationship between pain and depression. A suitably designed longitudinal study to examine causality would be a relevant step in the research agenda.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".