The Known Biology of Neuropathic Pain and Its Relevance to Pain Management
Bibliographic record
Abstract
Patients with neuropathic pain are heterogeneous in pathophysiology, etiology, and clinical presentation. Signs and symptoms are determined by the nature of the injury and factors such as genetics, sex, prior injury, age, culture, and environment. Basic science has provided general information about pain etiology by studying the consequences of peripheral injury in rodent models. This is associated with the release of inflammatory cytokines, chemokines, and growth factors that sensitize sensory nerve endings, alter gene expression, promote post-translational modification of proteins, and alter ion channel function. This leads to spontaneous activity in primary afferent neurons that is crucial for the onset and persistence of pain and the release of secondary mediators such as colony-stimulating factor 1 from primary afferent terminals. These promote the release of tertiary mediators such as brain-derived neurotrophic factor and interleukin-1β from microglia and astrocytes. Tertiary mediators facilitate the transmission of nociceptive information at the spinal, thalamic, and cortical levels. For the most part, these findings have failed to identify new therapeutic approaches. More recent basic science has better mirrored the clinical situation by addressing the pathophysiology associated with specific types of injury, refinement of methodology, and attention to various contributory factors such as sex. Improved quantification of sensory profiles in each patient and their distribution into defined clusters may improve translation between basic science and clinical practice. If such quantification can be traced back to cellular and molecular aspects of pathophysiology, this may lead to personalized medicine approaches that dictate a rational therapeutic approach for each individual.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".