Therapeutic effect of ultra pulse carbon dioxide fractional laser on treating hypertrophic scar after burn
Bibliographic record
Abstract
Objective To study the therapeutic effect of ultra pulsed carbon dioxide fractional laser on treating hypertrophic scar after burn. Methods Forty-five patients with hypertrophic scar after burn were selected. Each laser treatment included an ultra-pulse mode and a Scaar FX mode scan irradiation, and the treatment was conducted once a month for 4 to 6 times. In the ultra-pulse irradiation, the energy was 150~175 mJ, frequency was 40 Hz, hole spacing was about 4~5 mm, and irradiation time per hole was 2~3 s, and in Scaar FX mode energy was 60~150 mJ, frequency 250 Hz, and density 1% to 3%. Before the first treatment and 6 months after the end of the treatment, the Vancouver scar scale (VSS) was used to score the scar morphology, and the visual analog scale (VAS) was used to score the scar pain and itching. The effect of the treatment was classified as significant, effective and invalid. In addition, adverse reactions occurred during the treatment were recorded. Results After six months of the treatments, the VSS score, VAS pain score, and VAS pruritus score of the patients were 4.16±1.72, 1.58±0.62, and 1.24±0.74, respectively, while the pre-treatment values were 10.17±1.96, 2.98±0.89, and 2.31±0.97, respectively. The differences were statistically significant (all P<0.01). The treatment was effective in all patients, and the effect was significant in 35 cases, effective in 10 cases, and no recurrence occurred within 6 months after treatment. In 3 patients, blisters appeared on the scar after the first treatment, and the blister collapsed to form a wound. After the dressing change, the blisters healed after 7 to 10 days. Conclusion Ultra pulsed carbon dioxide fractional laser is effective in treating hypertrophic scar after burn, with mild adverse reactions and no relapse, which is worthy of clinical promotion. Key words: Burns; Scar; Ultra pulsed CO2 fractional laser; Laser therapy
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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 | 0.000 | 0.000 |
| 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".