Ablative fractional resurfacing with laser‐facilitated steroid delivery for burn scar management: Does the depth of laser penetration matter?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: -AFL) penetration of pathological burn scars influences clinical outcomes. STUDY DESIGN/MATERIALS AND METHODS: -AFL settings (energy which correlates to penetration depths) on different outcome parameters was analysed. Patients were divided into five groups depending on minimal scar penetration depth. RESULTS: Seventy-eight patients (158 scars) had complete data allowing for analysis. Median scar thickness was 3,400 μm and median laser scar penetration depth was 900 μm. Scar penetration categories were as follows: 0-25% (n = 40), 25-50% (n = 67), 50-75% (n = 31), 75-100% (n = 8), >100% (n = 3) of scar thickness. The median reduction in maximum scar thickness was 800 μm following one treatment (P < 0.001). However, this effect depended on scar penetration depth, whereby scars that were penetrated ≥75% showed no significant improvement in scar thickness and those penetrated >100% indicated a tendency to become worse. Other assessed outcome parameters included: the Vancouver Scar Scale, the Patient and Observer Scar Assessment Scale, a neuropathic pain score (DN4 Pain Questionnaire), and a pruritus score (modified D4 Pruritus Score). All these factors showed significant improvement in the categories up to 75% scar penetration depth. CONCLUSIONS: -AFL scar penetration depth significantly influences subjective and objective pathologic burn scar modulation. The penetration depth of 51-75% achieves the greatest reduction in scar thickness. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.
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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) | 0.000 | 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) | 0.002 | 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".