Chronic Pain: Second, Do No Harm
Bibliographic record
Abstract
Pain may be undertreated--contributing to anguish, as reported by the World Health Organization. Pain may be overtreated--inadvertently contributing to drug addiction, drug diversion, and even death. Pain may be misunderstood-contributing to illness propagation, as reported in somatization literature. Pain words may even be presented as a tool of manipulation, where report of pain is verbiage in pursuit of utilitarian social consequence. Thus, primum non nocere--first, do no harm--is not easily achieved in the pharmacological treatment of pain, particularly in pain reported chronically. Herein, we examine the pharmacological treatment of chronic pain, and we suggest strategies for improved management that are based on solid principles derived from extensive experience which may protect against the problems derived from the vague and subjective nature of pain symptoms. Optimal treatment of chronic pain may be assisted by three paradigms: (1) an adequate model of appraisal, (2) treatment focused on pathophysiology (whether physical, psychosocial, or some combination of these), and (3) frequent reassessment of total social function. By these approaches, contribution to drug abuse, diversion, and life deterioration can be largely avoided. Whereas the emphasis here is pharmacological management, the principles may be more widely applied to other therapies of chronic pain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".