Neuropathic pain following traumatic spinal cord injury: Models, measurement, and mechanisms
Bibliographic record
Abstract
Neuropathic pain following spinal cord injury (SCI) is notoriously difficult to treat and is a high priority for many in the SCI population. Resolving this issue requires animal models fidelic to the clinical situation in terms of injury mechanism and pain phenotype. This Review discusses the means by which neuropathic pain has been induced and measured in experimental SCI and compares these with human outcomes, showing that there is a substantial disconnection between experimental investigations and clinical findings in a number of features. Clinical injury level is predominantly cervical, whereas injury in the laboratory is modeled mainly at the thoracic cord. Neuropathic pain is primarily spontaneous or tonic in people with SCI (with a relatively smaller incidence of allodynia), but measures of evoked responses (to thermal and mechanical stimuli) are almost exclusively used in animals. There is even the question of whether pain per se has been under investigation in most experimental SCI studies rather than simply enhanced reflex activity with no affective component. This Review also summarizes some of the problems related to clinical assessment of neuropathic pain and how advanced imaging techniques may circumvent a lack of patient/clinician objectivity and discusses possible etiologies of neuropathic pain following SCI based on evidence from both clinical studies and animal models, with examples of cellular and molecular changes drawn from the entire neuraxis from primary afferent terminals to cortical sensory and affective centers. © 2016 Wiley Periodicals, Inc.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".