Neuropathic pain feature in cancer-induced bone pain: does it matter? a prospective observational study
Bibliographic record
Abstract
Background: Cancer-induced bone pain (CIBP) is considered to have both nociceptive and neuropathic components.However, the prevalence, risk factors, and impact of the neuropathic components are yet poorly understood.Methods: We estimate the prevalence of neuropathic pain (NP) features in patients with CIBP at a tertiary care pain clinic setting using the Douleur Neuropathique 4 questionnaire and evaluate their associated factors and their impact after 4 weeks of treatment using the Brief Pain Inventory questionnaire and the Edmonton Symptom Assessment System.Results: A total of 133 patients were recruited.The estimated prevalence of NP was 30.8% (95% confidence interval: 23.6%-39.1%).Initially, the patients with NP had significantly higher average pain scores (6.00 vs. 5.05, P = 0.006), higher total interference scores (5.84 vs. 4.89, P = 0.033), and symptom distress scores (35.88 vs. 26.52,P = 0.002).After 4 weeks of treatment, patients in both groups reported significantly decreased pain intensity and improved quality of life.However, the patients with NP still reported significantly higher average pain (4.61 vs. 3.58, P = 0.048), trending toward higher total interference scores (3.52 vs. 2.99, P = 0.426), and symptom distress scores (23.30 vs. 20.77,P = 0.524).From multivariate analysis, the independent risk factors for NP were younger age, pain in the extremities, and higher average pain scores.Conclusions: NP are common in patients with CIBP.These conditions negatively affect pain intensity and the patient's quality of life before and after treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".