Can recent chronic pain techniques help with acute perioperative pain?
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article discussed how the knowledge and technique of a few chronic pain procedures benefited the perioperative clinicians in their care of patients receiving specific orthopaedic surgical procedures. RECENT FINDINGS: Recent emerging interest in hip and knee denervation for chronic pain management secondary to osteoarthritis stimulates publications on the new understanding of hip and knee joint innervation. The improved understanding of the anatomy allows better precision in targeting the articular branches. The procedures for chronic joint pain such as radiofrequency ablation, chemical neurolysis and neuromodulation procedure have recently been applied to the perioperative care in orthopaedic procedures because of the potential long-lasting analgesia, opioid-sparing effect and consequent improvement in physical function and health-related quality of life after surgery. SUMMARY: Despite the widespread use of regional anaesthesia and multimodal analgesia in the perioperative pain management, more than two-third of the patients reported severe postoperative pain. Therefore, other therapeutic strategies used in chronic pain management such as radiofrequency ablation and neuromodulation have been proposed to optimize acute postsurgical pain. The early experience with those techniques is encouraging, and more studies are required to explore the incorporation of these procedures in the perioperative care.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".