Advances in the Management of Acute Postsurgical Pain: A Review
Bibliographic record
Abstract
Despite the millions of surgeries performed every year around the world, postoperative pain remains prevalent and is often addressed with inadequate or suboptimal treatments. Chronic postsurgical pain is surprisingly prevalent, and its rate varies with the type of surgery, as well as with certain patient characteristics. Thus, better clinical training is needed as well as patient education. As pain can be caused by more than one mechanism, multimodal or balanced postsurgical analgesia is appropriate. Pharmacological agents such as opioid and nonopioid pain relievers, as well as adjuvants and nonpharmacologic approaches, can be combined to provide better and opioid-sparing pain relief. Many specialty societies have guidelines for postoperative pain management that emphasize multimodal postoperative analgesia. These guidelines are particularly helpful when dealing with special populations such as pregnant patients or infants and children. Pediatric pain control, in particular, can be challenging as patients may be unable to communicate their pain levels. A variety of validated assessment tools are available for diagnosis. Related to therapy, most guidelines agree on the fact that codeine should be used with extreme caution in pediatric patients as some may be "rapid metabolizers" and its use may be life-threatening. Prehabilitation is a preoperative approach that prepares patients in advance of elective surgery with conditioning exercises and other interventions to optimize their health. Prehabilitation may have aerobic, strength-training, nutritional, and counseling components. Logistical considerations and degree of patient adherence represent barriers to effective prehabilitation programs. Notwithstanding all this, acute postoperative pain represents a clinical challenge that has not yet been well addressed.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".