Bibliographic record
Abstract
PURPOSE OF REVIEW: Interventional techniques were the mainstay for cancer pain management before the WHO ladder and opioids were freely available. The three-step WHO ladder has its limitations, and cancer pain is often under treated. Advances in treatment options mean that cancer patients are living longer and pain interventions may have a role to play even early in the cancer diagnosis for better quality of analgesia. The role of high doses of opioids in pain management is also currently under scrutiny. RECENT FINDINGS: Recent advances in intrathecal analgesia, radiofrequency techniques, both in tumour ablation and neurotomies, are being widely used for palliation. Vertebroplasty techniques have been used not only for pain relief, but also for stabilization. Improved imaging and thoracoscopic techniques have made coeliac plexus and splanchnic blockade safer and more efficacious. There has been recent interest in percutaneous cordotomy with newer techniques using computed tomography/MRI and endoscopy guidance. Percutaneous electrical nerve stimulation and 8% capsaicin patches have been successfully used for managing neuropathic pain in cancer. SUMMARY: Interventions form an integral part in providing pain relief in complex cancer pains. Oncologists and palliative care physicians are to be educated on the usefulness and timing of interventions in the management of complex cancer pain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.007 | 0.003 |
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".