OPRM1 receptor as new biomarker to help the prediction of post mastectomy pain and recurrence in breast cancer.
Bibliographic record
Abstract
Breast cancer is the most common type of cancer among women worldwide. Short-term postsurgical recovery is complicated by many factors, including imbalanced inflammatory and immune response, acute pain associated with functional impairment, and chronic postmastectomy pain (CPMP), developed by about 25-60% of patients. Opioids, most common drugs used for treatment of cancer pain, are immunosuppressive, and therefore, they might directly and/or indirectly influence long-term cancer recurrence. Moreover, they also produce endocrinopathy, which consists primarily of hypothalamic-pituitary-gonadal axis or hypothalamic-pituitary-adrenal axis dysfunction. The interindividual variability in both CPMP and opioid response is believed to be largely underlined by genetic variability in the gene locus for μ-opioid receptor (OPRM1) that modulates opioid pharmacodynamics. For this reason, OPRM1 genotype may play a key role both in short-term postmastectomy outcome and in long-term follow-up, becoming a new biomarker for breast cancer recurrence in patients suffering from chronic postmastectomy pain managed by opioid therapy. Hence OPRM1 might be used in near future to customize the opioid therapy, avoiding not only opioid side effects but also the disease progression. In this review we evaluate the literature state of the art on this topic and possible steps towards obtaining the safest individualized postmastectomy analgesic therapy. Therefore, a personalized pain treatment strategy might be useful to both manage pain and control cancer disease progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".