The Role of Hypericum Perforatum (St. John's Wort Oil) and Olive Oil in the Prevention of Peritoneal Adhesion in a Rat Model: An Animal Study
Bibliographic record
Abstract
Objective: We aimed to investigate Hypericum Perforatum (St. John's Wort Oil) and pure olive oil with respect to their effect on preventing intra-abdominal adhesions after abdominal surgery.Methods: This was an animal study carried out by the Giresun Faculty of Medicine, Department of General Surgery between August 10, 2020, and September 10, 2020. In the study, intra-abdominal adhesion development was examined macroscopically and microscopically on the 14th day after experimental abdominal surgery in three groups [(i) Control, (ii) olive oil application, (iii) St. John's Wort oil application] each consisting of 7 female Wistar Albino rats. Majuzi classification and Zuhlke's microscopic adhesion classification was used to evaluate adhesions.Results: Macroscopically, fibrous bands were not observed in 1 rat in the control group, 2 rats in olive oil recipients, and 1 rat in St. John's Wort oil recipients. A significant difference between the groups was identified in terms of Zuhlke's microscopic adhesion scores (p = 0.026). Accordingly, the adhesion level was significantly reduced in olive oil recipients compared to controls (p = 0.002). There was no significant difference in terms of adhesion between all groups (p>0.05). The Majuzi adhesion classifications in the control group, olive oil group and St. John's wort groups were also similar (p = 0.308).Conclusion: In rats who underwent abdominal surgery, it was determined that intra-abdominal olive oil application reduced the level of adhesion, while St. John's Wort oil application had no effect on adhesion formation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".