A Primary Care Provider’s Guide to Pain After Spinal Cord Injury: Screening and Management
Bibliographic record
Abstract
Individuals with spinal cord injury (SCI) often experience chronic pain as a secondary complication. It can significantly impair mental health, sleep, mood, and overall quality of life. It is important for providers within a primary care setting to recognize the different types of pain such as nociceptive and neuropathic. Various assessment tools are available to guide proper classification and subsequent management. Providers need to have a good knowledge base, structure, and patient focus when managing care. Nonpharmacological interventions are just as important and should be explored prior to or along with pharmacological interventions. Treatment modalities such as physical therapy, exercise, acupuncture, and cognitive behavioral therapy should be tailored to the individual to the greatest extent possible. Gabapentin, pregabalin, and amitriptyline have been studied extensively and are the first-line pharmacological agents for neuropathic pain. It is important to involve patients as equal stakeholders in any pain intervention with adequate lifelong follow-up. The aim of this article is to offer an overview of pain assessment, information, patient interaction, and treatment options available. Although chronic pain has remained difficult to treat successfully, primary care providers can play an integral role in delivering evidence-based and patient-centered care for managing chronic pain among individuals with SCI.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.035 |
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".