Bibliographic record
Abstract
Recent publications have suggested that more than two million adults in the United States suffer from neuropathic pain, but this number seems to be a significant underestimate. The prevalence of neuropathic pain from diabetes and postherpetic neuralgia alone, using the most conservative estimates of incidence, would equal two million Americans. Lesions of the nervous system responsible for pain genesis can occur either in the central or the peripheral nervous system. The most common causes of peripheral neuropathic pain syndromes worldwide are diabetes, HIV infection, cancer‐related neuropathy (due to tumour invasion, surgical nerve damage, radiation or chemotherapy‐induced nerve damage) and lumbar degenerative disc disease. Other less common, but significant, sources of suffering are postherpetic neuralgia, complex regional pain syndrome, phantom limb pain and postsurgical nerve trauma. Central neuropathic pain can be caused by stroke (infarct or hemorrhage), multiple sclerosis, spinal cord injury and syringomyelia. Certain pain syndromes such as trigeminal neuralgia and vulvodynia, although clearly neuropathic and a source of tremendous suffering, are not discussed in the present article due to space constraints. There is an unmet need for the treatment of neuropathic pain as evidenced by reports of pain despite the use of opioids and anticonvulsants, continuing psychological difficulties, lack of access to treatments and patients seeking access to complementary therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".