Neuropathic cancer pain: Prevalence, severity, analgesics and impact from the European Palliative Care Research Collaborative–Computerised Symptom Assessment study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Neuropathic pain causes greater pain intensity and worse quality of life than nociceptive pain. There are no published data that confirm this in the cancer population. AIM: We hypothesised that patients with neuropathic cancer pain had more intense pain, experienced greater suffering and were treated with more analgesics than those with nociceptive cancer pain, and a neuropathic pain screening tool, painDETECT, would perform as well in those with cancer pain as is reported in those with non-cancer pain. DESIGN: The data were obtained from an international cross-sectional observational study. SETTING/PARTICIPANTS: A total of 1051 patients from inpatients and outpatients, with incurable cancer completed a computerised assessment on symptoms, function and quality of life. In all, 17 centres within eight countries participated. Medical data were recorded by physicians. Pain type was a clinical diagnosis recorded on the Edmonton Classification System for Cancer Pain. RESULTS: Of the patients, 670 had pain: 534 with nociceptive pain, 113 with neuropathic pain and 23 were unclassified. Patients with neuropathic cancer pain were significantly more likely to be receiving oncological treatment, strong opioids and adjuvant analgesia and have a reduced performance status. They reported worse physical, cognitive and social function. Sensitivity and specificity of painDETECT for identifying neuropathic cancer pain was less accurate than when used in non-cancer populations. CONCLUSIONS: Neuropathic cancer pain is associated with a negative impact on daily living and greater analgesic requirements than nociceptive cancer pain. Validated assessment methods are needed to enable early identification of neuropathic cancer pain, leading to more appropriate treatment and reduced burden on patients.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it