Knowledge and Practice of Scar Treatment Among Health Care Physicians in Saudi Arabia
Bibliographic record
Abstract
Background The processes of wound healing and scar formation are complex phenomena that are determined by an intricate interplay of molecules and cells. A deviation from the anticipated trajectory of scarring can lead to the formation of hypertrophic scars and keloids. A wide range of therapeutic methodologies have been employed in the treatment of scars. This research paper seeks to enhance patient outcomes and the efficacy of scar repair as a whole by determining the knowledge of scar treatment and implementation in clinical practice in Saudi Arabia and thereby incorporating scientific findings into practical settings. Materials and methods This cross-sectional study, which included 237 participants, aimed to provide descriptive data on the knowledge and common practice of Saudi Arabian healthcare physicians with regard to scar prevention, treatment, and evaluation during the period from November 15, 2023, to December 11, 2023. Results In routine clinical practice, the most commonly employed subjective method for scar assessment is patient and observer scar assessment (162 (68.4%)) while the Modified Vancouver Scar Scale (91 (38.4%)) was commonly used for research purposes. However two-dimensional photography is the most frequently employed objective method in clinical practice (54 (22.8%)) and biomechanical properties (58 (24.5%)) for research purposes. Silicone scar therapy in the form of sheets or gel is the primary preventive measure in the prevention of keloids/hypertrophic scars across various patient populations. Corticosteroid injections and silicone are primary interventions within the initial 18-month period. Conclusion Although significant progress has been made in the field of scar management, standardization of procedures and increased adherence to evidence-based guidelines are still required.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".