Neuromodulation techniques for cancer pain management
Bibliographic record
Abstract
PURPOSE OF REVIEW: Advanced pain management techniques may be indicated in 5-15% of cancer patients. Despite this, a recent review identified that, over the course of 1 year in England, only 458 patients received a procedure intended to provide analgesia and only 30 patients had intrathecal drug delivery (ITDD) devices implanted. This article describes the emerging evidence for ITDD in cancer pain and provides a narrative review of other neuromodulatory techniques (including spinal cord stimulation, peripheral nerve stimulation and acupuncture), approaches that might be employed to address this area of significant unmet clinical need. RECENT FINDINGS: Numerous studies have been published within the last year reporting positive outcomes associated with ITDD in cancer pain management. Neuromodulation represents an important strategy in the management of persistent pain. Whilst the nonmalignant pain evidence-base is rapidly growing, it remains sparse for cancer pain management. The growing cohort of cancer survivors may significantly benefit from neuromodulatory techniques. SUMMARY: ITDD and other neuromodulatory techniques for cancer pain management appear underutilised in the UK and offer the prospect of better treatment for cancer patients with refractory pain or intolerable side-effects from systemic analgesics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".