Management of Chronic Pain in the Rheumatic Diseases with Insights for the Clinician
Bibliographic record
Abstract
Pain that accompanies musculoskeletal conditions should be regarded as an illness entity in its own right and deserves treatment in parallel with the management of the underlying condition. Recent understanding of the pathophysiology of rheumatic pain invokes interplay of the nociceptive mechanisms driven by local tissue factors and the neurogenic responses that sustain chronic pain. In line with other pain conditions, ideal treatment of rheumatic pain should be through a multimodal approach, integrating nonpharmacologic as well as pharmacologic treatments. In the light of this new concept of pain mechanisms, future pharmacologic treatment options may encompass a wider scope than the use of traditional analgesics and nonsteroidal anti-inflammatory drugs. There is currently limited experience for use of pharmacologic treatments that act primarily on neurogenic mechanisms in rheumatic conditions. Drug combination studies are lacking, but this strategy seems clinically reasonable to allow for an approach to treating pain from different mechanistic perspectives. An added advantage would be the opportunity to use lower doses of individual drugs and thereby reduce the side effect profile. Ideal pain management must also include attention to the important co-associates of pain such as effects on sleep, mood and energy, which all have an impact on the global burden of suffering. Although complete relief of pain is still an unrealistic objective, reasonable outcome goals for symptom relief should be accompanied with an improvement in function.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".