Neuropathic Pain Components in Patients with Cancer: Prevalence, Treatment, and Interference with Daily Activities
Bibliographic record
Abstract
BACKGROUND: Pain and neuropathic symptoms impact quality of life of patients with cancer. To obtain more insight in the prevalence, severity, and treatment of neuropathic symptoms in patients with cancer and their interference with daily activities, we conducted a cross-sectional study at the outpatient clinic of a Dutch university hospital. METHODS: A cross-sectional study among outpatients with cancer. To identify pain, its intensity, quality, and interference with daily activities, the Brief Pain Inventory (BPI) was used. Neuropathic symptoms were identified with the Douleur Neuropathique (DN4) interview and pain characteristics with the McGill Pain Questionnaire (MPQ). Pain medication and adjuvant analgesics were also collected with a prestructured questionnaire. Descriptives, chi-squared tests, t-tests, and a logistic regression analysis were conducted. RESULTS: 892 patients completed the questionnaires. Twenty-three percent (n = 204) reported moderate to severe pain, and 19% (n = 170) scored positive on neuropathic symptoms (DN4 ≥ 3). Particularly in patients with a rating on a numeric rating scale (NRS) < 5, existence of neuropathic symptoms significantly increased interference with daily activities. Of patients with neuropathic symptoms, 8% received adjuvant pain treatment. Receiving curative treatment, using a systemic drug with neurotoxicity, having had an operation, and having had a lymph node dissection independently contributed to having neuropathic symptoms. CONCLUSIONS: This study shows that over 40% of the patients with moderate to severe pain also have neuropathic symptoms, causing increased interference with daily activities. Most of these patients do not receive adjuvant analgesics. There is a need to improve management of neuropathic symptoms in patients with cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".