Progression of Neuropathic Pain after Acute Spinal Cord Injury: A Meta-Analysis and Framework for Clinical Trials
Bibliographic record
Abstract
The translation of therapeutic interventions to humans with spinal cord injury with the goal of promoting growth and repair in the central nervous system could, inadvertently, drive mechanisms associated with the development of neuropathic pain. A framework is needed to evaluate the probability that a therapeutic intervention for acute spinal cord injury modifies the progression of neuropathic pain. We analyzed a large, longitudinal dataset from the European Multi-Center Study about Spinal Cord Injury (EMSCI) and compared these observations with a previously published Swedish/Danish cohort. A meta-analysis was performed to produce aggregate estimates for the transition period between 1-6 months and the transition period between 1-12 months after injury. A secondary analysis used logistic regression to explore associations between the progression of neuropathic pain and demographics, pain characteristics, and injury characteristics. For overall neuropathic pain, 72% presenting with pain symptoms at one month reported persisting symptoms at six months, and 23% who did not have neuropathic pain at one month later had it develop. From 1-12 months, there was a similar likelihood of pain persisting (69%) and slightly higher rate of pain developing (36%). Characteristics that were significantly associated with the progression of pain included age and sensory and motor preservation. We provide historical benchmarks for estimating the progression of neuropathic pain during the first year after acute SCI. This information will be useful for comparison and evaluating safety during early phase acute spinal cord injury trials.
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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 | 0.270 | 0.304 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.050 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".