Early Interventional Treatments for Patients with Cancer Pain: A Narrative Review
Bibliographic record
Abstract
Between 10% and 20% of patients with cancer-related pain cannot achieve adequate control following the three-step ladder guidelines by the World Health Organization. Therefore, a "fourth step", including interventional approaches, has been suggested for those cases. Systematic reviews support the early use of interventional procedures to treat refractory cancer pain, control symptoms and prevent opioid dose escalation. There is strong evidence of the efficacy of celiac plexus or splanchnic neurolysis, vertebroplasty, kyphoplasty and intrathecal drug delivery. Those procedures have been found to be associated with a decrease in the symptom burden and opioid consumption, improved quality of life, and suggested as having a potentially positive impact on survival. Several studies have recommended using specific interventional techniques at earlier stages, possibly even when opioid treatment is first being considered. Conversely, leaving these options as a last analgesic resource might not be advisable since the burden these procedures might impose on too ill patients is significant. The objective of this review was to collect the available evidence published on the use of interventional treatments for refractory cancer pain with a particular interest in comparing early versus late indications. The results of the search demonstrated a very low number and quality of articles particularly addressing this question. This scarce number of evidence precluded performing a systematic analysis. A detailed and narrative description of the potential benefits of integrating interventional techniques into clinical guidelines at the early stages of the disease is provided.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".