Prevention of Post-Operative Adhesions: A Comprehensive Review of Present and Emerging Strategies
Bibliographic record
Abstract
Post-operative adhesions affect patients undergoing all types of surgeries. They are associated with serious complications, including higher risk of morbidity and mortality. Given increased hospitalization, longer operative times, and longer length of hospital stay, post-surgical adhesions also pose a great financial burden. Although our knowledge of some of the underlying mechanisms driving adhesion formation has significantly improved over the past two decades, literature has yet to fully explain the pathogenesis and etiology of post-surgical adhesions. As a result, finding an ideal preventative strategy and leveraging appropriate tissue engineering strategies has proven to be difficult. Different products have been developed and enjoyed various levels of success along the translational tissue engineering research spectrum, but their clinical translation has been limited. Herein, we comprehensively review the agents and products that have been developed to mitigate post-operative adhesion formation. We also assess emerging strategies that aid in facilitating precision and personalized medicine to improve outcomes for patients and our healthcare system.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".