Bibliographic record
Abstract
The pathophysiology of central pain syndromes is still poorly understood and their treatment remains a major challenge. Despite the development of several animal models of spinal cord injury pain, human studies are necessary to better link mechanisms to neuropathic symptoms and signs. Over the past years, several significant clinical studies have been devoted specifically to the mechanisms of central pain, particularly with regards to allodynia, in humans. These studies have used psychophysics, functional or morphological neuroimagery and electrophysiology. This workshop will outline the recent advances in our understanding of central pain mechanisms from human studies. Nanna Finnerup (Denmark) will present results of psychophysical and MRI studies obtained in spinal cord injury patients with at level and below level neuropathic pain, emphasizing the role of neuronal hyperexcitability at injury or higher level in spontaneous pain and allodynia. Roland Peyron (France) will present the contribution of functional imagery in the undestanding of the mechanisms of mechanical/cold allodynia and its relief after cortical stimulation in patients with central pain. Jonathan Dostrovsky (Canada) will present clinical and electrophysiological evidence from human studies supporting the role of the thalamus in central pain.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 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".