Long-Term Efficacy and Safety of Adhesion Prevention Agents in Abdominal and Pelvic Surgeries: A Systematic Review
Bibliographic record
Abstract
This systematic review evaluates the long-term efficacy and safety of adhesion prevention agents in abdominal and pelvic surgeries, synthesizing data from randomized controlled trials and meta-analyses. Adhesions, common postoperative complications, can lead to significant morbidity, including chronic pain, infertility, and bowel obstruction. Various agents, including hyaluronic acid-carboxymethylcellulose films and icodextrin solutions, have been developed to mitigate these risks. Our review highlights that agents like bioresorbable membranes (Seprafilm) and icodextrin significantly reduce the incidence and severity of adhesions, particularly in high-risk surgeries. However, certain complications such as anastomotic leaks and infections are associated with some agents, emphasizing the need for careful consideration in clinical decision-making. Additionally, while these agents reduce postoperative morbidity and enhance recovery, further research is needed to assess their long-term impact, particularly regarding fertility outcomes and chronic pain. This review underscores the importance of integrating adhesion prevention agents into surgical protocols, which has the potential to reduce healthcare costs, improve patient outcomes, and optimize postoperative care pathways. Standardization of adhesion prevention practices could further enhance surgical efficiency and patient recovery, particularly in high-risk patient populations and complex surgeries.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".