The CanPain SCI clinical practice guidelines for rehabilitation management of neuropathic pain after spinal cord injury: 2021 update
Bibliographic record
Abstract
STUDY DESIGN: Clinical practice guidelines. OBJECTIVES: The objective was to update the 2016 version of the Canadian clinical practice guidelines for the management of neuropathic pain in people with spinal cord injury (SCI). SETTING: The guidelines are relevant for inpatient, outpatient and community SCI rehabilitation settings in Canada. METHODS: The guidelines were updated in accordance with the Appraisal of Guidelines for Research and Evaluation II tool. A Steering Committee and Working Group reviewed the relevant evidence on neuropathic pain management (encompassing screening and diagnosis, treatment and models of care) after SCI. The quality of evidence was scored using Grading of Recommendations Assessment, Development and Evaluation (GRADE). A consensus process was followed to achieve agreement on recommendations and clinical considerations. RESULTS: The working group identified and reviewed 46 additional relevant articles published since the last version of the guidelines. The panel agreed on 3 new screening and diagnosis recommendations and 8 new treatment recommendations. Two key changes to these treatment recommendations included the introduction of general treatment principles and a new treatment recommendation classification system. No new recommendations to model of care were made. CONCLUSIONS: The CanPainSCI recommendations for the management of neuropathic pain after SCI should be used to inform practice.
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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 | 0.063 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".