Patients’ Perspectives on Pain
Bibliographic record
Abstract
Nociceptive and neuropathic pain (NP) are common consequences following spinal cord injury (SCI), with large impact on sleep, mood, work, and quality of life. NP affects 40% to 50% of individuals with SCI and is sometimes considered the major problem following SCI. Current treatment recommendations for SCI-NP primarily focus on pharmacological strategies suggesting the use of anticonvulsant and antidepressant drugs, followed by tramadol and opioid medications. Unfortunately, these are only partly successful in relieving pain. Qualitative studies report that individuals with SCI-related long-lasting pain seek alternatives to medication due to the limited efficacy, unwanted side effects, and perceived risk of dependency. They spend time and money searching for additional treatments. Many have learned coping strategies on their own, including various forms of warmth, relaxation, massage, stretching, distraction, and physical activity. Studies indicate that many individuals with SCI are dissatisfied with their pain management and with the information given to them about their pain, and they want to know more about causes and strategies to manage pain. They express a desire to improve communication with their physicians and learn about reliable alternative sources for obtaining information about their pain and pain management. The discrepancy between treatment algorithms and patient expectations is significant. Clinicians will benefit from hearing the patient´s voice.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".