Bibliographic record
Abstract
Chronic pain after surgery, also known as chronic postsurgical pain (CPSP), is recognized as a significant public health issue with serious medical and economic consequences. Current research on CPSP underscores the significant roles of both peripheral and central sensitization in pain development and maintenance. Peripheral sensitization occurs at the site of injury, through the hyperexcitability of nerve fibers due to surgical damage and the release of inflammatory mediators. This leads to increased expression of pronociceptive ion channels and receptors, such as transient receptor potential and acid-sensing ion channels (ASIC), enhancing pain signal transmission. Central sensitization involves long-term changes in the central nervous system, particularly in the spinal cord. In this context, sensitized spinal neurons become more responsive to pain signals, driven by continuous nociceptive input from the periphery, which results in an enhanced pain response characterized by hyperalgesia and/or allodynia. Key players in this process include N-methyl-D-aspartate receptor and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors, along with proinflammatory cytokines and chemokines released by activated glia. These glial cells release substances that further increase neuronal excitability, maintaining the sensitized state and contributing to persistent pain. The activation of antinociceptive systems is required for the resolution of pain after surgery, and default in these systems may also be considered as an important component of CPSP. In this review, we will examine the clinical factors underlying CPSP in patients and the mechanisms previously established in preclinical models of CPSP that may explain how acute postoperative pain may transform into chronic pain in patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".