The <scp>COMT rs</scp>4680 <scp>M</scp>et allele contributes to long‐lasting low back pain, sciatica and disability after lumbar disc herniation
Bibliographic record
Abstract
BACKGROUND: The COMT enzyme metabolizes catecholamines and thus modulates adrenergic, noradrenergic and dopaminergic signaling. A functional polymorphism in the gene encoding this enzyme, i.e. the COMT Val158Met SNP that reduces enzyme activity, has previously been linked to pain sensitivity. METHODS: We examined if the COMT Val158Met SNP could contribute to discogenic subacute low back pain and sciatica by comparing the frequency of the Val158Met genotypes of degenerative disc disease patients with healthy controls. Moreover, we examined if this SNP could predict the clinical outcome, i.e. the progression of pain and disability. RESULTS: The present data demonstrated that there were no differences in COMT genotype frequencies between the newly diagnosed patients and controls. Analysis of pain and disability in the patients over time revealed, however, a significant or border-line significant increase in McGill sensory score and Oswestry Disability Index (ODI) score for individuals with COMT Met/Met genotype. Furthermore, significant associations between the COMT Met-allele and VAS activity score, McGill sensory score and ODI score were observed in the patients 6 months after inclusion. DISCUSSION: Although the Val158Met SNP was not a risk factor for disc herniation, patients with Met/Met had more pain and slower recovery than those with Val/Met, which in turn also had more pain and slower recovery than those with Val/Val suggesting the SNP contributes to the progression of the symptoms of disc herniation. CONCLUSION: We conclude that the functional COMT Val158Met SNP contributes to long lasting low back pain, sciatica and disability after lumbar 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".