Pain Intensity the First Year after Lumbar Disc Herniation Is Associated with the A118G Polymorphism in the Opioid Receptor Mu 1 Gene: Evidence of a Sex and Genotype Interaction
Bibliographic record
Abstract
Earlier studies have shown that the single nucleotide polymorphism (SNP) A118G (rs1799971) in the opioid receptor mu 1 (OPRM1) gene may affect pain sensitivity. In the present study we investigated whether the A118G SNP could predict clinical outcome regarding progression of pain intensity and disability in patients with low back pain and sciatica after lumbar disc herniation. Patients (n = 258) with lumbar disc herniation and sciatic pain, all European-Caucasian, were recruited from two hospitals in Norway. Pain and disability were rated on a visual analog scale (VAS), by McGill Sensory Questionnaire and by Oswestry Disability Index (ODI) over a 12 months period. The data revealed a significant interaction between sex and A118G genotype regarding the pain intensity during the 12 months (VAS, p = 0.002; McGill, p = 0.021; ODI, p = 0.205, repeated-measures ANOVA). We found that */G women had a slower recovery rate than the */G men. Actually, the */G women had 2.3 times as much pain as the */G men 12 months after the disc herniation (VAS, p = 0.043, one-way ANOVA; p = 0.035, Tukey HSD). In contrast, the A/A women and A/A men seemed to have almost exactly the same recovery rate. The present data suggest that OPRM1 G allele increases the pain intensity in women, but has a protective effect in men the first year after disc herniation.
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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 | 0.001 | 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".