Comparison of pain quality descriptors in cancer patients with nociceptive and neuropathic pain.
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to explore the differences in the descriptors for neuropathic and nociceptive pain in cancer patients. PATIENTS AND METHODS: One hundred and eighty-six cancer patients who participated in the study completed the Greek version of the McGill Pain Questionnaire (G-MPQ) for the assessment of their pain quality. RESULTS: Significant differences were found between type of pain in all G-MPQ classes. Statistically significant associations were found between Present Pain Intensity and type of pain (p = 0.002). Multivariate logistic regression analyses showed that patients who selected the descriptors "pricking" and "annoying" were 2.64 times and 2.2 times, respectively, more likely to experience nociceptive rather than neuropathic pain (p = 0.020 and p = 0.015, respectively). Further analysis showed that sensory seemed to be the most significant indicator for type of pain (95%, CI: 0.911-0.974, p < 0.001). CONCLUSION: Sensory quality and some of pain descriptors may differentiate neuropathic from nociceptive pain in cancer patients.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".