Bibliographic record
Abstract
UNLABELLED: The objectives of this study are to evaluate the magnitude of postsurgical adhesions clinically, their impact on the hospital budget, and the available adhesion-reducing substances. We conducted a review of relevant literature on intraabdominal adhesion, adhesion-reducing substances, and their related cost. The relevance of adhesions to gynecology not only relates to infertility and abdominal pain, but also to the occurrence of bowel obstruction. There have been many substances and materials used to decrease the adhesion formation. However, there is still no adhesion-reducing substance that is unequivocally effective. Its use is also costly. The most common cause of small-bowel obstruction is postsurgical adhesions. Indeed, more than one half of patients with adhesion-related small-bowel obstruction had previous gynecologic operations, and a high percentage occurs after abdominal hysterectomy. To date, there has been no study suggesting that the use of adhesion-reducing substances decreases the risk of bowel obstruction or the long-term costs to the healthcare system. The long-awaited substance or barrier that unequivocally prevents adhesion formation is yet to come. TARGET AUDIENCE: Obstetricians & Gynecologists, Family Physicians. LEARNING OBJECTIVES: After completion of this article, the reader should be able to list the causes of small bowel obstruction, to describe the commercially available adhesion-reducing substances, and to compare the efficacy of the various adhesion-reducing substances.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".