Bibliographic record
Abstract
Animal models of chronic pain are widely used to investigate basic mechanisms of chronic pain and to evaluate potential novel drugs for treating chronic pain. Among the different criteria used to measure chronic pain, behavioral responses are commonly used as the end point measurements. However, not all chronic pain conditions can be easily measured by behavioral responses such as the headache, phantom pain and pain related to spinal cord injury. Here I propose that cortical indexes, that indicate neuronal plastic changes in pain-related cortical areas, can be used as endpoint measurements for chronic pain. Such cortical indexes are not only useful for those chronic pain conditions where a suitable animal model is lacking, but also serve as additional screening methods for potential drugs to treat chronic pain in humans. These cortical indexes are activity-dependent immediate early genes, electrophysiological identified plastic changes and biochemical assays of signaling proteins. It can be used to evaluate novel analgesic compounds that may act at peripheral or spinal sites. I hope that these new cortical endpoint measurements will facilitate our search for new, and more effective, pain medicines, and help to reduce false lead drug targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".