Cancer Induced Bone Pain: Pain Classification and Management
Bibliographic record
Abstract
Pain is a multidimensional personal experience influenced by biological, psychological, social and cultural factors. In the context of cancer pain, particularly cancer-induced bone pain (CIBP), it is essential that clinicians assess the multidimensional nature of pain to fully understand the patient’s pain experience, which often involves an evoked sense of both vulnerability and mortality. Bone metastases are highly prevalent in advanced cancer, resulting in CIBP being one of the most common cancer pain syndromes. CIBP affects a patient’s physical and social functioning, reduces quality of life, increases psychological distress and often results in hospice or hospital admission. Yet, there is limited evidence to guide analgesic management. The complex pathophysiology and varied clinical presentation of CIBP warrant personalised management guided through standardised assessment. In this thesis, the candidate used the Edmonton Classification System for Cancer Pain (ECS-CP) to assess the multidimensional experience of pain in patients with bone metastases. The five assessed domains were correlated with pain intensities and opioid requirements. Additionally, the candidate identified refractory CIBP and recruited these patients to examine the possible efficacy of methadone rotation (MR) compared to other opioid rotations (OOR) in a pilot randomised controlled study. The studies reveal the challenge in managing CIBP lies in the high prevalence of breakthrough pain, which was associated with neuropathic pain, higher average and worst pain intensities and higher opioid requirements. Opioid rotation, including rotation to methadone, is safe and acceptable to patients while reducing pain intensities and pain interference in daily life. It also suggests that MR may have the additional benefits of providing earlier and more sustained pain reduction, particularly for worst pain intensity, with the utilisation of lower opioid equivalent doses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".