Bibliographic record
Abstract
BACKGROUND: Burn scars are common in the paediatric population. When involving the face, it diminishes quality of life. Ablative fractional laser (AFL) therapy is becoming the preferred choice for established scars due to its greater potential depth for thermal injury (4 mm), which leads to photothermolysis with subsequent neocollagenesis and collagen fibre realignment and remodelling. Combined with small z-plasties and topical steroids, it has been proven to: flatten and decrease the volume of scars, increase pliability and decrease pruritus and erythema. The purpose of the case series was to determine the clinical significance of a single session of AFL therapy, combined with small z-plasties and topical steroids on facial scars post burn injury. METHOD: Four cases of paediatric facial scarring post burns were selected to undergo a single treatment of AFL therapy, accompanied by small z-plasties and topical steroids. Modified Vancouver Scar Scores (MVSS) pre- and postoperatively at 3 and 6 months were evaluated. RESULTS: Improvement of all components of the MVSS was achieved after 6 months, with major improvement in scar pliability and symptomatology. The mean MVSS improved from 14 (range 12-16) preoperatively to 5 and 5.5 respectively at 3 and 6 months postoperatively. Non-parametric analysis with Friedman Two-Way ANOVA by Rank showed a statistical significance between the pre- and postoperative MVSS (p = 0.024). CONCLUSION: AFL should form an integral part of the burn scar armamentarium.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.265 | 0.149 |
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".