Commentary: Wired for pain? Understanding brain connectivity and socioemotional factors in adolescents and young adult females following orthopedic surgery
Bibliographic record
Abstract
Sieberg and colleagues (this issue) deliver valuable insights into the underlying neural and psychosocial mechanisms of chronic post-surgical pain (CPSP) in female adolescents and young adults (AYAs). CPSP is persistent pain that extends beyond 2 months following surgery and has been increasingly recognized as a critical outcome of orthopedic procedures (Einhorn et al., 2024). In addition to known psychological risk factors (e.g., anxiety, depression, pain-related worry), central nervous system functions, including central sensitization and pain inhibitory processing, seem to play a crucial role in the development of CPSP (Chow et al., 2020; Sieberg et al., 2022). AYA females appear disproportionately impacted by CPSP, with higher incidence, greater pain sensitivity, functional disruptions, and in some cases, reduced effectiveness, as well as side effects from standard treatments (i.e., opioid medications) (Angst et al., 2012; Casale et al., 2021; Kanaan et al., 2021). Critically, there is a dearth of research on the interactions of biological, psychological, and social mechanisms contributing to the development, persistence, and treatment of CPSP in AYA females.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".