Risk factors and clinical significance of refractory pain in patients with bone metastases: a comprehensive meta-analysis
Bibliographic record
Abstract
Background: Refractory cancer pain, especially bone pain, presents a major clinical challenge that is difficult to manage despite the use of multimodal analgesic strategies. This meta-analysis aims to estimate the prevalence of refractory cancer pain in this patient population and to identify potential predictors that may increase the likelihood of developing such pain. In addition, we performed a systematic review of previous studies that delve into more effective pain strategies. Methods: This meta-analysis and systematic review were conducted in accordance with the PRISMA guidelines. A comprehensive search was performed using PubMed, Web of Science, Embase, and the Cochrane Library on risk factors for refractory metastatic bone pain. The inclusion criteria focused on studies reporting the incidence and/or risk factors associated with refractory cancer pain, providing relevant statistical measures such as odds ratios (OR), hazard ratios (HR), or relative risks (RR). The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS), and a random-effects meta-analysis was conducted using the R programming language. Results: = 2.7198). The analysis also identified several critical predictors of refractory cancer pain. The presence of multiple bone metastases was consistently linked to an increased likelihood of refractory cancer pain with an OR of 3.94 (95% CI: 2.64-5.87). Similarly, lytic bone metastases demonstrated a high OR of 5.99 (95% CI: 3.17-11.30). Furthermore, there was a strong correlation between the occurrence of refractory cancer pain with severe acute pain (OR = 219.20, 95% CI: 0.26-188127.63), breakthrough pain (OR = 16.44, 95% CI: 0.60-448.07), and psychological comorbidities such as depression (OR = 3.91, 95% CI: 1.22-2048.64) and anxiety (OR = 4.22, 95% CI: 1.22-2048.64). Conclusion: Refractory cancer pain, observed in approximately 70% of patients with bone metastases, poses a significant clinical challenge. Refractory cancer pain predictors include the presence of multiple and lytic bone metastases, severe acute pain, breakthrough pain, and psychological comorbidities. Collectively, our findings highlight the need for improved pain management strategies that address both the physical and psychological aspects of cancer pain.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.072 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".