Endogenous Opioid Inhibition of Chronic Low-Back Pain Influences Degree of Back Pain Relief After Morphine Administration
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Factors underlying differential responsiveness to opioid analgesic medications used in chronic pain management are poorly understood. We tested whether individual differences in endogenous opioid inhibition of chronic low-back pain were associated with the magnitude of acute reductions in back pain ratings after morphine administration. METHODS: In randomized counterbalanced order over three sessions, 50 chronic low-back pain patients received intravenous naloxone (8 mg), morphine (0.08 mg/kg), or placebo. Back pain intensity was rated predrug and again after peak drug activity was achieved using the McGill Pain Questionnaire-Short Form (Sensory and Affective subscales, VAS Intensity measure). Opioid blockade effect measures to index degree of endogenous opioid inhibition of back pain intensity were derived as the difference between predrug to postdrug changes in pain intensity across placebo and naloxone conditions, with similar morphine responsiveness measures derived across placebo and morphine conditions. RESULTS: Morphine significantly reduced back pain compared with placebo (McGill Pain Questionnaire-Short Form Sensory, VAS; P < 0.01). There were no overall effects of opioid blockade on back pain intensity. However, individual differences in opioid blockade effects were significantly associated with the degree of acute morphine-related reductions in back pain on all measures, even after controlling for effects of age, sex, and chronic pain duration (P < 0.03). Individuals exhibiting greater endogenous opioid inhibition of chronic back pain intensity reported less acute relief of back pain with morphine. CONCLUSIONS: Morphine appears to provide better acute relief of chronic back pain in individuals with lower natural opioidergic inhibition of chronic pain intensity. Possible implications for personalized medicine are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".