Bibliographic record
Abstract
BACKGROUND: Each year, over 100 million patients are afflicted with new scars from medical procedures worldwide. Natural compounds have shown promise in the treatment of scars and skin disorders. Rosehip oil (RO), produced from the pressed fruit of the rosehip (Rosa canina L.) plant, is used in the pharmaceutical, cosmetic, and food industries. The use of this plant in the treatment of scars has yet to be reviewed. AIMS: This review aims to analyze the current findings on the use of RO in the treatment of postsurgical scars. METHODS: This literature search considered published journal articles (clinical trials or literature reviews). Studies were identified by searching electronic databases (PubMed and MEDLINE) and reference lists of respective articles. Additional articles were identified through Google Scholar. Only articles available in English were included in this review. RESULTS: There is a scarcity of high-quality studies assessing the therapeutic potential of RO. From the two human clinical trials using RO, there is some evidence to suggest its potential as an active ingredient in topical formulations for the treatment of wounds. Topical treatments containing RO extract may reduce the size and erythema of postsurgical scars through the polarization of macrophages and the inhibition of inflammatory cytokines. CONCLUSIONS: Some evidence suggests that RO may improve postsurgical scars. At present, there is insufficient evidence to recommend the use of RO for the treatment of wounds. Further investigation is required to establish its therapeutic effects on human skin and its potential use as an ingredient in topical formulations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.000 | 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 teacher head, 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".