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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".