Complex Regional Pain Syndrome in Cancer Cases: Current Knowledge and Perspectives
Bibliographic record
Abstract
Background: Complex regional pain syndrome (CRPS) is a disabling painful disorder caused by many different and poorly understood mechanisms. It often affects the distal limbs and usually happens as consequence of a trauma. Its severity can remarkably affect patients' quality of life. When this painful complication happens in a cancer patient, the impact may be exponential. To date, there is limited knowledge of the surrounding circumstances of CRPS cases in this population. Methods: We present two clinical cases of patients diagnosed with cancer-related pain presenting with symptoms and signs compatible with CRPS. In one case, CRPS was attributed to direct tumor nerve compression, and it responded successfully to an interventional pain procedure. The second case was associated with a Zoster infection in an immunocompromised cancer patient. Patient responded to multidisciplinary pain management strategies. Additionally, we conducted a literature review to investigate the coexistence of cancer pain and CRPS and suggest some pathophysiology mechanisms of action. Results and Discussion: Literature reviewed and potential pathophysiology mechanisms are simultaneously explored in terms of classification, etiopathology, evidence, challenges, and future scientific directions. Conclusion: Comorbid CRPS can impact negatively in cases of cancer pain by affecting their diagnosis and treatment. Further studies are necessary to elucidate how these two conditions present together and how they can be better addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".