Epidemiology of Resistant Cancer Pain: Prevalence, Clinical Burden, and Treatment Gaps
Bibliographic record
Abstract
BACKGROUND: Resistant cancer pain (RCP) remains a challenge in oncology, affecting patients whose pain persists despite guideline-based treatment. While advancements in pharmacological and interventional strategies have improved cancer pain management, barriers such as opioid access restrictions, provider knowledge gaps, and underutilization of specialized pain interventions contribute to inadequate relief. Understanding the epidemiology, classification, and risk factors for RCP is essential for improving treatment. SUMMARY: This review examines the prevalence, pathophysiology, and burden of RCP, highlighting its impact on quality of life and healthcare systems. Pain severity is commonly assessed using numerical rating scales, but comprehensive frameworks like the Edmonton Classification System for Cancer Pain (ECS-CP) provide better insight into complex pain syndromes. Breakthrough pain, neuropathic pain, and cancer-induced bone pain are frequently linked to treatment resistance. While opioids remain central to pharmacological management, many patients require multimodal approaches, including adjuvant analgesics, interventional procedures, and radiation therapy. Neurosurgical options such as cordotomy, intrathecal drug delivery, and myelotomy offer pain relief in select cases but are underutilized due to limited awareness and training. KEY MESSAGES: RCP remains a major unmet medical need, affecting many cancer patients despite advances in pain management. Effective treatment requires a multimodal, individualized approach integrating pharmacological, interventional, and neurosurgical strategies. While neurosurgical interventions provide substantial relief in selected patients, their use is often limited by referral delays and lack of provider awareness. Overcoming systemic barriers, refining pain classification, and expanding access to specialized pain management are essential to improving RCP care.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".