The Effects of Aloe Vera Cream on the Alar Scar in Rhinoplasty, A Randomized Double-Blind Controlled Trial.
Bibliographic record
Abstract
Introduction: Many studies have been done on the use of aloe vera in wound healing, but fewer studies were done on the influence of this material on the reduction of the alar scar. Therefore, we evaluated the effect of a newly made aloe vera cream on alar wound healing after rhinoplasty. Materials and Methods: This was a randomized, double-arm, parallel-group, double-blind controlled trial and was done from June 2021 to February 2022. External wedge resection was done for all patients. The patients were randomly assigned to receive aloe vera cream (n=31) (intervention group) or Face Doux cream (comparison group) (n = 29). A pharmacist prepared the aloe vera cream. The primary outcome measure was the wound scar status which was assessed by two Questionnaires, including the mean Patient Scar Assessment Questionnaire (PSAQ) and Vancouver Scar Scale (VSS). Randomization and Blinding were done. Results: The mean PSAQ was significantly lower in group A after two weeks (26.9 versus 31.5, P<0.001), after two months (15.7 versus 19.6, P=0.04), and six months follow-up (8.8 versus 11.8, P=0.005). The mean VSS was significantly lower in group A after two weeks (5.6 versus 7.1, P=0.001), after two months (3.5 versus 4.9, P=0.002), and six months (1.2 versus 2.7, P<0.001). Repeated measurement analysis showed that both interventions significantly affected PSAQ and VSS. Conclusion: Although both interventions had a significant effect on PSAQ and VSS, compared to Face Duox, the topical use of Aloe Vera cream significantly reduced scar formation after alar resection, both statistically and clinically.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".