Advancing research and clinical care in the management of neuropathic pain after spinal cord injury: Key findings from a Canadian summit
Bibliographic record
Abstract
Background: Optimal management of neuropathic pain (NP) is essential to enhancing health-related quality of life for individuals living with spinal cord injury (SCI). A key strategic priority for the Ontario Neurotrauma Foundation (ONF) and Rick Hansen Institute (RHI) is optimizing NP management after SCI.Aims: A National Canadian Summit, sponsored by ONF and RHI, was held to develop a strategic plan to improve NP management after SCI.Methods: In a one-day meeting held in Toronto, Ontario, a multidisciplinary panel of 18 Canadian stakeholders utilized a consensus workshop methodology to (1) describe the current state of the field, (2) create a long-term vision, and (3) identify steps for moving into action.Results: A review of the current state of the field identified strengths including rigourously developed evidence syntheses and practice landscape documentation. Identified gaps included limited evidence on NP hindering recommendation development in evidence syntheses, absence of a national strategy, care silos with limited cross-continuum connections, limited consumer involvement, and limited practice standard implementation. The panel identified key themes for a long-term vision to improve the management of SCI NP in Canada, including establishing an integrated collaborative network; standardized care and outcome evaluation; education; advocacy; and directing resources to innovative solutions. The panel identified the next step as prioritization of areas that will have the greatest impact in a 5-year time frame.Conclusion: A strategic plan outlining a long-term vision to improve management of NP after SCI in Canada was developed and will inform future activities of the sponsors.
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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 | 0.124 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".