Bibliographic record
Abstract
Introduction Neuropathic pain occurs frequently in patients with cancer, both from direct tumor infiltration of neural structures and as a consequence of treatment of the neoplasm. Management of neuropathic pain presents a number of challenges. Such pain is often more resistant to conventional analgesic approaches than is nociceptive pain. Many of these patients will have mixed pain problems, where neuropathic pain is combined with elements of somatic or visceral nociceptive pain. Neuropathic pain also may signal progressive and often incurable disease, adding a significant suffering component to the pain problem. Successful management of neuropathic cancer pain requires an understanding of the pathophysiologic processes that generate this type of pain and of the distinctive clinical features that identify it. In addition, a knowledge of the various clinical neuropathic pain syndromes that occur in the cancer patient and of the range of available treatments is essential. Pathophysiology Neuropathic pain occurs as a result of aberrant somatosensory processing in the nervous system, and as such may be sustained by peripheral mechanisms, central mechanisms, or both. Pain after peripheral nerve injury may occur through a variety of mechanisms. When a nerve is compressed or distended, nerve trunk pain may occur as a result of activation of the nervi nervorum, the normal nociceptive afferents that innervate the nerve sheaths themselves (1). Damage to primary nociceptive afferents may result in spontaneous ectopic activity, perhaps secondary to focal demyelination with exposure of sodium channels (2). Regenerating afferents may form neuromas, where sodium channels accumulate and spontaneous activity occurs (3). The dorsal root ganglion may represent an additional site of ectopic activity (3).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.023 |
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".