Microsurgical principles and postoperative adhesions: lessons from the past
Bibliographic record
Abstract
"Microsurgery" is a set of principles developed to improve fertility surgery outcomes. These principles were developed progressively based on common sense and available evidence, under control of clinical feedback obtained with the use of second-look laparoscopy. Fertility outcome was the end point; significant improvement in fertility rates validated the concept clinically. Postoperative adhesion formation being a major cause of failure in fertility surgery, the concept of microsurgery predominantly addresses prevention of postoperative adhesions. In this concept, magnification with a microscope or laparoscope plays a minor role as technical facilitator. Not surprisingly, the principles to prevent adhesion formation are strikingly similar to our actual understanding: gentle tissue handling, avoiding desiccation, irrigation at room temperature, shielding abdominal contents from ambient air, meticulous hemostasis and lavage, avoiding foreign body contamination and infection, administration of dexamethasone postoperatively, and even the concept of keeping denuded areas separated by temporary adnexal or ovarian suspension. The actual concepts of peritoneal conditioning during surgery and use of dexamethasone and a barrier at the end of surgery thus confirm without exception the tenets of microsurgery. Although recent research helped to clarify the pathophysiology of adhesion formation, refined its prevention and the relative importance of each factor, the clinical end point of improvement of fertility rates remains demonstrated for only the microsurgical tenets as a whole. In conclusion, the principles of microsurgery remain fully valid as the cornerstones of reproductive microsurgery, whether performed by means of open access or laparoscopy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".