The American Society of Pain and Neuroscience (ASPN) Best Practices and Guidelines for the Interventional Management of Cancer-Associated Pain
Bibliographic record
Abstract
Moderate to severe pain occurs in many cancer patients during their clinical course and may stem from the primary pathology, metastasis, or as treatment side effects. Uncontrolled pain using conservative medical therapy can often lead to patient distress, loss of productivity, shorter life expectancy, longer hospital stays, and increase in healthcare utilization. Various publications shed light on strategies for conservative medical management for cancer pain and a few international publications have reviewed limited interventional data. Our multi-institutional working group was assembled to review and highlight the body of evidence that exists for opioid utilization for cancer pain, adjunct medication such as ketamine and methadone and interventional therapies. We discuss neurolysis via injections, neuromodulation including targeted drug delivery and spinal cord stimulation, vertebral tumor ablation and augmentation, radiotherapy and surgical techniques. In the United States, there is a significant variance in the interventional treatment of cancer pain based on fellowship training. As a first of its kind, this best practices and interventional guideline will offer evidenced-based recommendations for reducing pain and suffering associated with malignancy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".