Strategies Aimed at Preventing Chronic Post-surgical Pain: Comprehensive Perioperative Pain Management after Total Joint Replacement Surgery
Bibliographic record
Abstract
PURPOSE: Chronic post-surgical pain (CPSP) is a frequent outcome of musculoskeletal surgery. Physiotherapists often treat patients with pain before and after musculoskeletal surgery. The purposes of this paper are (1) to raise awareness of the nature, mechanisms, and significance of CPSP; and (2) to highlight the necessity for an inter-professional team to understand and address its complexity. Using total joint replacement surgeries as a model, we provide a review of pain mechanisms and pain management strategies. SUMMARY OF KEY POINTS: By understanding the mechanisms by which pain alters the body's normal physiological responses to surgery, clinicians selectively target pain in post-surgical patients through the use of multi-modal management strategies. Clinicians should not assume that patients receiving multiple medications have a problem with pain. Rather, the modern-day approach is to manage pain using preventive strategies, with the aims of reducing the intensity of acute postoperative pain and minimizing the development of CPSP. CONCLUSIONS: The roles of biological, surgical, psychosocial, and patient-related risk factors in the transition to pain chronicity require further investigation if we are to better understand their relationships with pain. Measuring pain intensity and analgesic use is not sufficient. Proper evaluation and management of risk factors for CPSP require inter-professional teams to characterize a patient's experience of postoperative pain and to examine pain arising during functional activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".