A randomized prospective study on the clinical effect of two treatments on keloids
Bibliographic record
Abstract
Objective To evaluate the effect of two treatment approaches on keloids: fractional CO2 laser combined with32 P applicator,and fractional CO2 laser combined with intralesional injection of compound betamethasone.Methods A total of 70 patients were divided into two groups: group A( n = 36) treated with fractional CO2 laser combined with32 P applicator,and group B( n = 34) treated with fractional CO2 laser combined with intralesional injection of compound betamethasone. All patients received fractional CO2 laser therapy first and then recombinant human epidermal growth factor gel. After the wound healed,patients in group A received32 P applicator therapy,0. 4-0. 8 MBQ /cm2 for 96 hours; patients in group B received compound betamethasone injection therapy,once every month for 3 times.The clinical effect,symptoms,satisfaction and ratio of side effects between the two groups were compared. Results The cure rate and satisfaction in group A were significantly higher than those in group B( χ2= 5. 884,t = 2. 193,P 0. 05). The Vancouver Scar Scale( VSS) scores,pain scores,and itch scores were significantly improved in both groups after treatment( t = 30. 070,14. 797,11. 889; t = 18. 049,10. 016,8. 290,P 0. 01); the above items in group A were significantly lower than those in group B after comprehensive intervention( t = 5. 861,5. 480,3. 069,P 0. 01). Conclusion Fractional CO2 laser combined with32 P applicator is highly effective on keloids with satisfactory clinical results. However,the technique still needs improvement.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".