Bibliographic record
Abstract
Postsurgical adhesion formation is an important clinical problem within all surgical specialties. In gynecology, adhesions resulting from gynecologic procedures are a major clinical, social, and economic concern because they may result in pelvic pain, infertility, or bowel obstruction. In addition, it may lead to additional surgery to resolve the adhesion-related complications. This review evaluates the available evidence regarding the effectiveness of various strategies for reducing postsurgical adhesions. Those strategies include surgical techniques and adhesion-reducing substances. Postsurgical adhesions are natural consequences of tissue trauma and healing. Our review indicates that most of the effective adhesion-reducing substances decrease adhesion formation and reformation, but they do not prevent its occurrence. In fact, there is no single modality proven to be unequivocally effective in preventing adhesion formation. Current evidence suggests that the use of ORC (Interceed; Gynecare, Somerville, NJ), e-PTFE (Gore-Tex Surgical Membrane, Preclude; WL Gore, Flagstaff, AZ), HA-CMC (Seprafilm; Genzyme, Cambridge, MA), or 4% icodextrin (Adept; Baxter BioSurgery, Deerfield, IL) is justified. Their use, however, should not replace good surgical techniques. We recommend the use of microsurgical principles, minimally invasive surgery, and the use of adhesion-reducing agents.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 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".