CACNG2 polymorphisms associate with chronic pain after mastectomy
Bibliographic record
Abstract
Chronic postmastectomy pain (PMP) imposes a major burden on the quality of life of the ever-increasing number of long-term survivors of breast cancer. An earlier report by Nissenbaum et al. claimed that particular polymorphisms in the gene CACNG2 are associated with the risk of developing chronic PMP after breast surgery (Nissenbaum J, Devor M, Seltzer Z, Gebauer M, Michaelis M, Tal M, Dorfman R, Abitbul-Yarkoni M, Lu Y, Elahipanah T, delCanho S, Minert A, Fried K, Persson AK, Shpigler H, Shabo E, Yakir B, Pisante A, Darvasi A. Susceptibility to chronic pain following nerve injury is genetically affected by CACNG2. Genome Res 2010;20:1180-90). This information is important because in principle, it can inform the surgical, radiological, and chemotherapeutic decision-making process in ways that could mitigate the increased risk of chronic pain. In this study, we revisited this claim by independently evaluating the proposed marker haplotype using 2 different patient cohorts recruited in different research settings. Meta-analysis of these new postmastectomy cohorts and the original cohort confirmed significant association of the CACNG2 haplotype with PMP. In addition, we tested whether the same markers would predict chronic postsurgical pain in men who underwent surgery for inguinal hernia repair, and whether there is significant genetic association with cutaneous thermal sensitivity in postmastectomy and postherniotomy patients. We found that the biomarker is selective because it did not predict pain after laparoscopic hernia repair and was not associated with pain sensitivity to experimentally applied noxious thermal stimuli. We conclude that the A-C-C haplotype at the 3 single-nucleotide polymorphisms (rs4820242, rs2284015, and rs2284017) in the CACNG2 gene is associated with increased risk of developing PMP. This information may advance current knowledge on pathophysiology of PMP and serve as a step forward in the prediction of clinical outcomes and personalized pain management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".