Bibliographic record
Abstract
Adhesion formation following surgical procedures poses a major clinical challenge, often leading to chronic pain, increased hospital readmissions, and substantial healthcare costs exceeding a billion dollars annually. Despite advancements in surgical techniques, minimally invasive approaches, and barrier methods, there is a lack of targeted research on preventing secondary adhesions after adhesiolysis. This study aims to bridge this gap in the literature by exploring novel treatment options. In our earlier research, alanyl-glutamine was shown to effectively prevent primary adhesions in a rat model involving polypropylene-type meshes, which are typically associated with severe adhesions despite their suitability for hernia repair. Building on these findings, we extended our investigation to determine if this treatment could also prevent secondary adhesions post-adhesiolysis. In this study, adhesions were induced in Wistar rats, followed by adhesiolysis and subsequent treatment with alanyl-glutamine. Six weeks posttreatment, the extent of adhesion formation was evaluated, revealing no adhesion formation. Our results demonstrate that alanyl-glutamine effectively prevents both primary and secondary adhesions in a rat model, highlighting its potential as a promising intervention to mitigate the adverse effects and complications associated with surgical adhesions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".