Examining the epigenetic transmission of risk for chronic pain associated with paternal post-traumatic stress disorder: a focus on veteran populations
Bibliographic record
Abstract
Chronic pain is a public health problem that significantly reduces quality of life. Although the aetiology is often unknown, recent evidence suggests that susceptibility can be transmitted intergenerationally, from parent to child. Post-traumatic stress disorder (PTSD) is a debilitating psychological disorder, often associated with chronic pain, that has high prevalence rates in military personnel and Veterans. Therefore, we aimed to characterise the epigenetic mechanisms by which paternal trauma, such as PTSD, is transmitted across generations to confer risk in the next generation, specifically focusing on Veterans where possible. Numerous overlapping neurological pathways are implicated in both PTSD and chronic pain; many of which are susceptible to epigenetic modification, such as DNA methylation, histone modifications, and RNA regulation. Hence, epigenetic changes related to pain perception, inflammation, and neurotransmission may influence an individual's predisposition to chronic pain conditions. We also examine the effects of PTSD on parenting behaviours and discuss how these variations could impact the development of chronic pain in children. We highlight the need for further research regarding the interactions between paternal trauma and epigenetic processes to ultimately generate effective prevention and therapeutic strategies for Veterans who have been affected by PTSD and chronic pain.
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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 | 0.001 | 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 | 0.000 |
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".