Efficacy and Safety of intralesional injection of verapamil in treatment of keloids
Bibliographic record
Abstract
Objective: The aim of this study is to evaluate the efficacy of intralesional verapamil injection in treating keloids. In addition to, assessment of side effects of the therapy. Patients and Methods: A Prospective study (Before& After) conducted for the period one year (April 2020- April 2021) at Tishreen University Hospital in Lattakia- Syria. 30 patients with keloids who received treatment with verapamil were included in the study. Results: The mean age was 22±6.3 years, 66.7% of patients were females. Most common sites of keloids involvement were shoulders (46.7%), and acne vulgaris was the most frequently etiology (40%). A statistically significantly reduction in the means of: Vancouver Scar Scale VSS 4.66±1.9 (day 21 of last treatment) vs. 7.06±1.8 (Day 0), p: 0.0001), height (1.16±0.6 vs. 2.23±0.6, p: 0.001), and pliability (1.33±0.9 vs. 2.60±0.6, p: 0.003), without recurrence of keloids at the day 90 of the last treatments. Burning pain was the most side effect frequently seen (98%).There was no significant relationship between clinical improvement and each of the following: duration of disease and age of the patient (p>0.05). Conclusion: Verapamil has significant therapeutic effects in decreasing the height of the lesion, improving pliability without recurrence of lesions or serious adverse effects, thus, it could be a safe choice in treatment of keloids.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".